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 "autonomous reinforcement learning of multiple interrelated tasks"):
  Abstract: Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be interrelated (or "hierarchical") so that a robot has to first learn to achieve some of them to set the preconditions for learning other ones. Even though different strategies have been used in robotics to tackle the acquisition of interrelated tasks, in particular within the developmental robotics framework, autonomous learning in this kind of scenarios is still an open question. Building on previous research in the framework of intrinsically motivated open-ended learning, in this work we describe how this question can be addressed working on the level of task selection, in particular considering the multiple interrelated tasks scenario as an MDP where the system is trying to maximise its competence over all the tasks.

Includes members:
  Member "intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "curious ilqr resolving uncertainty in model based rl")
  Member "autonomous discovery of the goal space to learn a parameterized skill":
    also connected through other hyperedges:
      (none)
  Member "automatic goal generation for reinforcement learning agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "overcoming exploration in reinforcement learning with demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "intrinsically motivated goal exploration processes with automatic curriculum learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "active learning of inverse models with intrinsically motivated goal exploration in robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling map elites to deep neuroevolution")

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.LG(Machine Learning)