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 "minimax confidence interval for off policy evaluation and policy optimization"):
  Abstract: We study minimax methods for off-policy evaluation (OPE) using value-functions and marginalized importance weights. Despite that they hold promises of overcoming the exponential variance in traditional importance sampling, several key problems remain: #R##N#(1) They require function approximation and are generally biased. For the sake of trustworthy OPE, is there anyway to quantify the biases? #R##N#(2) They are split into two styles ("weight-learning" vs "value-learning"). Can we unify them? #R##N#In this paper we answer both questions positively. By slightly altering the derivation of previous methods (one from each style; Uehara and Jiang, 2019), we unify them into a single confidence interval (CI) that automatically comes with a special type of double robustness: when either the value-function or importance weight class is well-specified, the CI is valid and its length quantifies the misspecification of the other class. We can also tell which class is misspecified, which provides useful diagnostic information for the design of function approximation. Our CI also provides a unified view of and new insights to some recent methods: for example, one side of the CI recovers a version of AlgaeDICE (Nachum et al., 2019b), and we show that the two sides need to be used together and either alone may incur doubled approximation error as a point estimate. #R##N#We further examine the potential of applying these bounds to two long-standing problems: off-policy policy optimization with poor data coverage (i.e., exploitation), and efficient exploration. With a well-specified value-function class, we show that optimizing the lower and the upper bounds lead to good exploitation and exploration, respectively.

Includes members:
  Member "reinforcement learning via fenchel rockafellar duality":
    also connected through other hyperedges:
      (none)
  Member "gradientdice rethinking generalized offline estimation of stationary values":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "minimax weight and q function learning for off policy evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "off policy policy gradient with state distribution correction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an optimistic perspective on offline reinforcement learning")
  Member "understanding the curse of horizon in off policy evaluation via conditional importance sampling":
    also connected through other hyperedges:
      (none)
  Member "information theoretic considerations in batch reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "dualdice behavior agnostic estimation of discounted stationary distribution corrections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "algaedice policy gradient from arbitrary experience":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")

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)