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: "curiosity driven reinforcement learning with homeostatic regulation"
  Abstract: We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.

Connected through hyperedge HE-1 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"
    - "teacher student curriculum learning"

Connected through hyperedge HE-2 (induced by source paper "learning awareness models"):
  with members:
    - "distributed prioritized experience replay"
    - "parameter space noise for exploration"
    - "learning to represent haptic feedback for partially observable tasks"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "the consciousness prior"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-3 (induced by source paper "being curious about the answers to questions novelty search with learned attention"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "attention is all you need"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "count based exploration with neural density models"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "neural episodic control"
    - "learning body affordances to simplify action spaces"
    - "curiosity driven exploration by self supervised prediction"

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)