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: "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"
  Abstract: An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.

Connected through hyperedge HE-1 (induced by source paper "automated shapeshifting for function recovery in damaged robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "interoceptive robustness through environment mediated morphological development"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "a minimal developmental model can increase evolvability in soft robots"
    - "robots that can adapt like animals"

Connected through hyperedge HE-2 (induced by source paper "reset free trial and error learning for robot damage recovery"):
  with members:
    - "patchwork kriging for large scale gaussian process regression"
    - "quality and diversity optimization a unifying modular framework"
    - "limbo a fast and flexible library for bayesian optimization"
    - "reset free guided policy search efficient deep reinforcement learning with stochastic initial states"
    - "robots that can adapt like animals"
    - "trust region policy optimization"
    - "illuminating search spaces by mapping elites"
    - "evolving a behavioral repertoire for a walking robot"

Connected through hyperedge HE-3 (induced by source paper "map based multi policy reinforcement learning enhancing adaptability of robots by deep reinforcement learning"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "evolving deep neural networks"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "robust adversarial reinforcement learning"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "feudal networks for hierarchical reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-4 (induced by source paper "evolvability signatures of generative encodings beyond standard performance benchmarks"):
  with members:
    - "evolvability is inevitable increasing evolvability without the pressure to adapt"
    - "fast damage recovery in robotics with the t resilience algorithm"

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.AI(Artificial Intelligence)