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: "motion planning among dynamic decision making agents with deep reinforcement learning"
  Abstract: Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "emi exploration with mutual information"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems"):
  with members:
    - "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
    - "the limits and potentials of deep learning for robotics"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "solving pomdps by searching in policy space"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"
    - "discretized approximations for pomdp with average cost"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "learning robotic assembly from cad"
    - "learning navigation behaviors end to end with autorl"
    - "state representation learning for control an overview"
    - "motion planning networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deeply informed neural sampling for robot motion planning"

Connected through hyperedge HE-6 (induced by source paper "a deep multi agent reinforcement learning approach to autonomous separation assurance"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "starcraft ii a new challenge for reinforcement learning"
    - "social attention modeling attention in human crowds"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-7 (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"
    - "downwash aware trajectory planning for large quadrotor teams"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "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-8 (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"
    - "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"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"

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)