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: "emphatic temporal difference learning"
  Abstract: Emphatic algorithms are temporal-difference learning algorithms that change their effective state distribution by selectively emphasizing and de-emphasizing their updates on different time steps. Recent works by Sutton, Mahmood and White (2015), and Yu (2015) show that by varying the emphasis in a particular way, these algorithms become stable and convergent under off-policy training with linear function approximation. This paper serves as a unified summary of the available results from both works. In addition, we demonstrate the empirical benefits from the flexibility of emphatic algorithms, including state-dependent discounting, state-dependent bootstrapping, and the user-specified allocation of function approximation resources.

Connected through hyperedge HE-1 (induced by source paper "stabilizing off policy q learning via bootstrapping error reduction"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-2 (induced by source paper "recurrent value functions"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "reward estimation for variance reduction in deep reinforcement learning"
    - "q learning with nearest neighbors"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "starcraft ii a new challenge for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning values across many orders of magnitude"

Connected through hyperedge HE-3 (induced by source paper "is the policy gradient a gradient"):
  with members:
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "should all temporal difference learning use emphasis"):
  with members:
    - "online off policy prediction"
    - "a first empirical study of emphatic temporal difference learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-5 (induced by source paper "safe and efficient off policy reinforcement learning"):
  with members:
    - "q lambda with off policy corrections"
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"
    - "the arcade learning environment an evaluation platform for general agents"
    - "off policy learning with eligibility traces a survey"

Connected through hyperedge HE-6 (induced by source paper "unifying task specification in reinforcement learning"):
  with members:
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on convergence of emphatic temporal difference learning"
    - "multi timescale nexting in a reinforcement learning robot"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-7 (induced by source paper "q lambda with off policy corrections"):
  with members:
    - "safe and efficient off policy reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "data efficient off policy policy evaluation for reinforcement learning"):
  with members:
    - "doubly robust off policy value evaluation for reinforcement learning"

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)