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: "lets drive driving in a crowd by learning from tree search"
  Abstract: Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics. The robot vehicle must contend with a dynamic and partially observable environment, noisy sensors, and many agents. A principled approach is to formalize it as a Partially Observable Markov Decision Process (POMDP) and solve it through online belief-tree search. To handle a large crowd and achieve real-time performance in this very challenging setting, we propose LeTS-Drive, which integrates online POMDP planning and deep learning. It consists of two phases. In the offline phase, we learn a policy and the corresponding value function by imitating the belief tree search. In the online phase, the learned policy and value function guide the belief tree search. LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both. Experimental results in simulation show that LeTS-Drive outperforms either planning or imitation learning alone and develops sophisticated driving skills.

Connected through hyperedge HE-1 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts"):
  with members:
    - "meta learning of sequential strategies"
    - "meta learners learning dynamics are unlike learners"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "residual policy learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-2 (induced by source paper "summit a simulator for urban driving in massive mixed traffic"):
  with members:
    - "gamma a general agent motion prediction model for autonomous driving"
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "learning dexterous in hand manipulation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "carla an open urban driving simulator"
    - "playing for benchmarks"

Connected through hyperedge HE-3 (induced by source paper "relational graph learning for crowd navigation"):
  with members:
    - "learning transferable cooperative behavior in multi agent teams"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "sparse interacting gaussian processes efficiency and optimality theorems of autonomous crowd navigation"
    - "attention is all you need"
    - "vain attentional multi agent predictive modeling"
    - "a structured self attentive sentence embedding"
    - "the predictron end to end learning and planning"
    - "adam a method for stochastic optimization"

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)