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: "evolving a behavioral repertoire for a walking robot"
  Abstract: Numerous algorithms have been proposed to allow legged robots to learn to walk. However, the vast majority of these algorithms is devised to learn to walk in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which com-bines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of con-trollers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution 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 online adaptation in robotics through meta learning embeddings of simulated priors"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "reset free trial and error learning for robot damage recovery"
    - "on first order meta learning algorithms"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"

Connected through hyperedge HE-2 (induced by source paper "adaptive prior selection for repertoire based online adaptation in robotics"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "reset free trial and error learning for robot damage recovery"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "patchwork kriging for large scale gaussian process regression"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-3 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "procedural content generation through quality diversity"
    - "alphastar an evolutionary computation perspective"
    - "evaluating map elites on constrained optimization problems"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning and exploration of reachable outcome space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "from exploration to control learning object manipulation skills through novelty search and local adaptation"
    - "reset free trial and error learning for robot damage recovery"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-5 (induced by source paper "autonomous skill discovery with quality diversity and unsupervised descriptors"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "real world evolution adapts robot morphology and control to hardware limitations"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"
    - "quality and diversity optimization a unifying modular framework"
    - "robust lstm autoencoders for face de occlusion in the wild"

Connected through hyperedge HE-6 (induced by source paper "mapping hearthstone deck spaces through map elites with sliding boundaries"):
  with members:
    - "monte carlo tree search for simulation based strategy analysis"
    - "talakat bullet hell generation through constrained map elites"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "demonstrating the feasibility of automatic game balancing"
    - "robots that can adapt like animals"

Connected through hyperedge HE-7 (induced by source paper "evolved embodied phase coordination enables robust quadruped robot locomotion"):
  with members:
    - "real world evolution adapts robot morphology and control to hardware limitations"
    - "self modifying morphology experiments with dyret dynamic robot for embodied testing"

Connected through hyperedge HE-8 (induced by source paper "behavioural repertoire via generative adversarial policy networks"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "diversity is all you need learning skills without a reward function"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "improved training of wasserstein gans"
    - "quality and diversity optimization a unifying modular framework"

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)