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 "measuring sample path causal influences with relative entropy"):
  Abstract: We present a sample path dependent measure of causal influence between time series. The proposed causal measure is a random sequence, a realization of which enables identification of specific patterns that give rise to high levels of causal influence. We show that these patterns cannot be identified by existing measures such as directed information (DI). We demonstrate how sequential prediction theory may be leveraged to estimate the proposed causal measure and introduce a notion of regret for assessing the performance of such estimators. We prove a finite sample bound on this regret that is determined by the worst case regret of the sequential predictors used in the estimator. Justification for the proposed measure is provided through a series of examples, simulations, and application to stock market data. Within the context of estimating DI, we show that, because joint Markovicity of a pair of processes does not imply the marginal Markovicity of individual processes, commonly used plug-in estimators of DI will be biased for a large subset of jointly Markov processes. We introduce a notion of DI with "stale history", which can be combined with a plug-in estimator to upper and lower bound the DI when marginal Markovicity does not hold.

Includes members:
  Member "on the bias of directed information estimators":
    also connected through other hyperedges:
      (none)
  Member "a sample path measure of causal influence":
    also connected through other hyperedges:
      (none)
  Member "consistent tomography under partial observations over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning graph influence from social interactions")
  Member "k nn estimation of directed information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial orthogonal regression two non linear regressions for causal inference")
  Member "estimating the directed information and testing for causality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")
  Member "jidt an information theoretic toolkit for studying the dynamics of complex systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information decomposition based on cooperative game theory")
  Member "strong completeness and faithfulness in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings")
  Member "causal networks semantics and expressiveness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")

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.IT(Information Theory)