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: "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.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for synthesizing functions in higher order logic"):
  with members:
    - "cls smt bringing together combinatory logic synthesis and satisfiability modulo theories"
    - "hammering mizar by learning clause guidance"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "enigma efficient learning based inference guiding machine"
    - "tactictoe learning to reason with hol4 tactics"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"
    - "shielded decision making in mdps"

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.LG(Machine Learning)