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 "conditional dependence via shannon capacity axioms estimators and applications"):
  Abstract: We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables. We propose that an estimate of causal strength should be based on the conditional distribution of the effect given the cause (and not on the driving distribution of the cause), and study dependence measures on conditional distributions. Shannon capacity, appropriately regularized, emerges as a natural measure under these axioms. We examine the problem of calculating Shannon capacity from the observed samples and propose a novel fixed-$k$ nearest neighbor estimator, and demonstrate its consistency. Finally, we demonstrate an application to single-cell flow-cytometry, where the proposed estimators significantly reduce sample complexity.

Includes members:
  Member "demystifying fixed k nearest neighbor information estimators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finite sample analysis of image registration")
  Member "a non parametric k nearest neighbour entropy estimator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "missing features reconstruction and its impact on classification accuracy")
  Member "telling cause from effect in deterministic linear dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causality for machine learning")
  Member "estimating mutual information by local gaussian approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "demystifying fixed k nearest neighbor information estimators")
  Member "efficient estimation of mutual information for strongly dependent variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "distinguishing cause from effect using observational data methods and benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal discovery from incomplete data a deep learning approach")
  Member "nonparametric divergence estimation with applications to machine learning on distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "irregular convolutional auto encoder on point clouds")

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)