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: "uncertainty aware reinforcement learning for collision avoidance"
  Abstract: Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot. In this paper, we consider the specific case of a mobile robot learning to navigate an a priori unknown environment while avoiding collisions. In order to learn collision avoidance, the robot must experience collisions at training time. However, high-speed collisions, even at training time, could damage the robot. A successful learning method must therefore proceed cautiously, experiencing only low-speed collisions until it gains confidence. To this end, we present an uncertainty-aware model-based learning algorithm that estimates the probability of collision together with a statistical estimate of uncertainty. By formulating an uncertainty-dependent cost function, we show that the algorithm naturally chooses to proceed cautiously in unfamiliar environments, and increases the velocity of the robot in settings where it has high confidence. Our predictive model is based on bootstrapped neural networks using dropout, allowing it to process raw sensory inputs from high-bandwidth sensors such as cameras. Our experimental evaluation demonstrates that our method effectively minimizes dangerous collisions at training time in an obstacle avoidance task for a simulated and real-world quadrotor, and a real-world RC car. Videos of the experiments can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference"):
  with members:
    - "domain adversarial and conditional state space model for imitation learning"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "variational recurrent models for solving partially observable control tasks"
    - "deep dynamics models for learning dexterous manipulation"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model based reinforcement learning for atari"
    - "bayesian variational autoencoders for unsupervised out of distribution detection"

Connected through hyperedge HE-2 (induced by source paper "airsim drone racing lab"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "event based vision a survey"
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"

Connected through hyperedge HE-3 (induced by source paper "danger aware adaptive composition of drl agents for self navigation"):
  with members:
    - "multi advisor reinforcement learning"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-4 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-5 (induced by source paper "way off policy batch deep reinforcement learning of implicit human preferences in dialog"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "off policy deep reinforcement learning without exploration"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal action graph networks"):
  with members:
    - "charades ego a large scale dataset of paired third and first person videos"
    - "referring relationships"
    - "relational deep reinforcement learning"
    - "graph networks as learnable physics engines for inference and control"
    - "actor centric relation network"
    - "video action transformer network"
    - "relational inductive biases deep learning and graph networks"
    - "videos as space time region graphs"

Connected through hyperedge HE-7 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "one shot visual imitation learning via meta learning"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "time contrastive networks self supervised learning from video"

Connected through hyperedge HE-8 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"

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.LG(Machine Learning)