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 "induction of subgoal automata for reinforcement learning"):
  Abstract: In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL). Our method relies on inducing an automaton whose transitions are subgoals expressed as propositional formulas over a set of observable events. A state-of-the-art inductive logic programming system is used to learn the automaton from observation traces perceived by the RL agent. The reinforcement learning and automaton learning processes are interleaved: a new refined automaton is learned whenever the RL agent generates a trace not recognized by the current automaton. We evaluate ISA in several gridworld problems and show that it performs similarly to a method for which automata are given in advance. We also show that the learned automata can be exploited to speed up convergence through reward shaping and transfer learning across multiple tasks. Finally, we analyze the running time and the number of traces that ISA needs to learn an automata, and the impact that the number of observable events has on the learner's performance.

Includes members:
  Member "joint inference of reward machines and policies for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end robustness for sensing reasoning machine learning pipelines")
  Member "semantic rl with action grammars data efficient learning of hierarchical task abstractions":
    also connected through other hyperedges:
      (none)
  Member "representing formal languages a comparison between finite automata and recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints")
  Member "extracting automata from recurrent neural networks using queries and counterexamples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints")
  Member "a laplacian framework for option discovery in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "options of interest temporal abstraction with interest functions")
  Member "iterative learning of answer set programs from context dependent examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "making sense of sensory input")
  Member "symmetry breaking answer set solving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on local domain symmetry for model expansion")
  Member "hierarchical reinforcement learning with the maxq value function decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")

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)