human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
  Abstract: Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. #R##N#We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "robot navigation with map based deep reinforcement learning"):
  with members:
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "prioritized experience replay"

Connected through hyperedge HE-2 (induced by source paper "multi agent motion planning for dense and dynamic environments via deep reinforcement learning"):
  with members:
    - "force based algorithm for motion planning of large agent teams"
    - "trajectory generation for multiagent point to point transitions via distributed model predictive control"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "downwash aware trajectory planning for large quadrotor teams"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"

Connected through hyperedge HE-3 (induced by source paper "frozone freezing free pedestrian friendly navigation in human crowds"):
  with members:
    - "densecavoid real time navigation in dense crowds using anticipatory behaviors"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"

Connected through hyperedge HE-4 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates"):
  with members:
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "algorithms for verifying deep neural networks"
    - "worst cases policy gradients"
    - "learning controls using cross modal representations bridging simulation and reality for drone racing"
    - "oil observational imitation learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-6 (induced by source paper "a new framework for multi agent reinforcement learning centralized training and exploration with decentralized execution via policy distillation"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "actor attention critic for multi agent reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "counterfactual multi agent policy gradients"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-7 (induced by source paper "long range indoor navigation with prm rl"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deepnav learning to navigate large cities"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-8 (induced by source paper "multi agent deep reinforcement learning with adaptive policies"):
  with members:
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "flow a modular learning framework for autonomy in traffic"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergent complexity via multi agent competition"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.RO(Robotics)