human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "active learning for autonomous intelligent agents exploration curiosity and interaction"):
  Abstract: In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks. Different communities proposed different solutions, that are in many cases, similar and/or complementary. These solutions include active learning, exploration/exploitation, online-learning and social learning. The common aspect of all these approaches is that it is the agent to selects and decides what information to gather next. Applications for these approaches already include tutoring systems, autonomous grasping learning, navigation and mapping and human-robot interaction. We discuss how these approaches are related, explaining their similarities and their differences in terms of problem assumptions and metrics of success. We consider that such an integrated discussion will improve inter-disciplinary research and applications.

Includes members:
  Member "multi class generalized binary search for active inverse reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "active learning of inverse models with intrinsically motivated goal exploration in robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaling map elites to deep neuroevolution")
  Member "selecting the state representation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations in model free hierarchical reinforcement learning")
  Member "local utility elicitation in gai models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient based optimization for bayesian preference elicitation")
  Member "gaussian processes for sample efficient reinforcement learning with rmax like exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "output feedback online optimal control for a class of nonlinear systems")
  Member "variance based rewards for approximate bayesian reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "varibad a very good method for bayes adaptive deep rl via meta learning")
  Member "near optimal nonmyopic value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "safe exploration in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cautious reinforcement learning with logical constraints")

Question: Please tell me which class the source paper 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)