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: "learning to walk in every direction"
  Abstract: Numerous algorithms have been proposed to allow legged robots to learn to walk. However, their vast majority are devised to learn to walk along a straight line, which not sufficient to accomplish any real-world mission. #R##N#Here we introduce TBR-Learning, a new learning algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded, TBR-Learning is substantially faster than independently learning each controller. Our technique relies on two methods: (1) novelty search with local competition, which comes from the artificial life research field and (2) the transferability approach, which combines simulations and real tests to optimize a policy. We evaluate this new technique on a hexapod robot. Results show that with only a few dozens of short experiments performed on the physical robot, the algorithm learns a collection of controllers that allows the robot to reach each point of its reachable space. Overall, TBR-Learning opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

Connected through hyperedge HE-1 (induced by source paper "fast damage recovery in robotics with the t resilience algorithm"):
  with members:
    - "crossing the reality gap a short introduction to the transferability approach"
    - "evolutionary algorithms for 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)