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: "extropy complementary dual of entropy"
  Abstract: This article provides a completion to theories of information#R##N#based on entropy, resolving a longstanding question in its axiomatization as#R##N#proposed by Shannon and pursued by Jaynes. We show that Shannon’s entropy#R##N#function has a complementary dual function which we call “extropy.”#R##N#The entropy and the extropy of a binary distribution are identical. However,#R##N#the measure bifurcates into a pair of distinct measures for any quantity that is#R##N#not merely an event indicator. As with entropy, the maximum extropy distribution#R##N#is also the uniform distribution, and both measures are invariant with#R##N#respect to permutations of their mass functions. However, they behave quite#R##N#differently in their assessments of the refinement of a distribution, the axiom#R##N#which concerned Shannon and Jaynes. Their duality is specified via the relationship#R##N#among the entropies and extropies of course and fine partitions. We#R##N#also analyze the extropy function for densities, showing that relative extropy#R##N#constitutes a dual to the Kullback–Leibler divergence, widely recognized as#R##N#the continuous entropy measure. These results are unified within the general#R##N#structure of Bregman divergences. In this context they identify half the L2#R##N#metric as the extropic dual to the entropic directed distance. We describe a#R##N#statistical application to the scoring of sequential forecast distributions which#R##N#provoked the discovery.

Connected through hyperedge HE-1 (induced by source paper "multivariate dependence beyond shannon information"):
  with members:
    - "invariant components of synergy redundancy and unique information among three variables"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "understanding interdependency through complex information sharing"
    - "model free quantification of time series predictability"
    - "quantifying unique information"
    - "intersection information based on common randomness"
    - "bivariate measure of redundant information"
    - "anatomy of a bit information in a time series observation"

Connected through hyperedge HE-2 (induced by source paper "the bethe permanent of a nonnegative matrix"):
  with members:
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "a new class of upper bounds on the log partition function"
    - "the bethe partition function of log supermodular graphical models"
    - "pseudocodewords from bethe permanents"
    - "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions"
    - "partition functions of normal factor graphs"
    - "computing the permanent with belief propagation"

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.IT(Information Theory)