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 "adaptive temporal difference learning for policy evaluation with per state uncertainty estimates"):
  Abstract: We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods are known to achieve complementary bias-variance trade-off properties, with TD tending to achieve lower variance but potentially higher bias. In this paper, we argue that the larger bias of TD can be a result of the amplification of local approximation errors. We address this by proposing an algorithm that adaptively switches between TD and MC in each state, thus mitigating the propagation of errors. Our method is based on learned confidence intervals that detect biases of TD estimates. We demonstrate in a variety of policy evaluation tasks that this simple adaptive algorithm performs competitively with the best approach in hindsight, suggesting that learned confidence intervals are a powerful technique for adapting policy evaluation to use TD or MC returns in a data-driven way.

Includes members:
  Member "temporal difference learning with neural networks study of the leakage propagation problem":
    also connected through other hyperedges:
      (none)
  Member "efficient exploration through bayesian deep q networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "deep exploration via bootstrapped dqn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "adaptive lambda least squares temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning state based eligibility traces for more sample efficient policy evaluation")
  Member "a greedy approach to adapting the trace parameter for temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning state based eligibility traces for more sample efficient policy evaluation")
  Member "an emphatic approach to the problem of off policy temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")
  Member "the arcade learning environment an evaluation platform for general agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "off policy learning with eligibility traces a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algaedice policy gradient from arbitrary experience")

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)