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 "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"):
  Abstract: We propose a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires the attainment of an unknown sequence of high-level objectives. Our method employs a recently-published algorithm for synthesis of compact automata to uncover this sequential structure. We synthesise an automaton from trace data generated through exploration of the environment by the deep RL agent. A product construction is then used to enrich the state space of the environment so that generation of an optimal control policy by deep RL is guided by the discovered structure encoded in the automaton. Our experiments show that our method is able to achieve training results that are otherwise difficult with state-of-the-art RL techniques unaided by external guidance.

Includes members:
  Member "learning concise models from long execution traces":
    also connected through other hyperedges:
      (none)
  Member "logically constrained reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "exact finite state machine identification from scenarios and temporal properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning moore machines from input output traces")
  Member "logically constrained neural fitted q iteration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "omega regular objectives in model free reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "policy synthesis for factored mdps with graph temporal logic specifications")
  Member "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning non markovian reward models in mdps")
  Member "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "modular multitask reinforcement learning with policy sketches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchically decoupled imitation for morphological transfer")

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)