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: "understanding interdependency through complex information sharing"
  Abstract: The interactions between three or more random variables are often nontrivial, poorly understood and, yet, are paramount for future advances in fields such as network information theory, neuroscience and genetics. In this work, we analyze these interactions as different modes of information sharing. Towards this end, and in contrast to most of the literature that focuses on analyzing the mutual information, we introduce an axiomatic framework for decomposing the joint entropy that characterizes the various ways in which random variables can share information. Our framework distinguishes between interdependencies where the information is shared redundantly and synergistic interdependencies where the sharing structure exists in the whole, but not between the parts. The key contribution of our approach is to focus on symmetric properties of this sharing, which do not depend on a specific point of view for differentiating roles between its components. We show that our axioms determine unique formulas for all of the terms of the proposed decomposition for systems of three variables in several cases of interest. Moreover, we show how these results can be applied to several network information theory problems, providing a more intuitive understanding of their fundamental limits.

Connected through hyperedge HE-1 (induced by source paper "generalised measures of multivariate information content"):
  with members:
    - "a novel approach to multivariate redundancy and synergy"
    - "idtxl the information dynamics toolkit xl a python package for the efficient analysis of multivariate information dynamics in networks"
    - "quantifying high order interdependencies via multivariate extensions of the mutual information"
    - "unique information and secret key agreement"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "probability mass exclusions and the directed components of mutual information"
    - "invariant components of synergy redundancy and unique information among three variables"
    - "secret sharing and shared information"

Connected through hyperedge HE-2 (induced by source paper "information decomposition based on cooperative game theory"):
  with members:
    - "a novel approach to multivariate redundancy and synergy"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"
    - "multivariate dependence beyond shannon information"
    - "geometry of information integration"
    - "hierarchical quantification of synergy in channels"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"

Connected through hyperedge HE-3 (induced by source paper "data disclosure under perfect sample privacy"):
  with members:
    - "quantifying high order interdependencies via multivariate extensions of the mutual information"
    - "generative adversarial privacy"
    - "on perfect privacy and maximal correlation"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"

Connected through hyperedge HE-4 (induced by source paper "quantifying high order interdependencies via multivariate extensions of the mutual information"):
  with members:
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "multivariate dependence beyond shannon information"
    - "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems"
    - "quantifying synergistic mutual information"
    - "anatomy of a bit information in a time series observation"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-5 (induced by source paper "pointwise partial information decomposition using the specificity and ambiguity lattices"):
  with members:
    - "invariant components of synergy redundancy and unique information among three variables"
    - "secret sharing and shared information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "multivariate dependence beyond shannon information"
    - "on extractable shared information"
    - "coarse graining and the blackwell order"
    - "synergetic and redundant information flow detected by unnormalized granger causality application to resting state fmri"
    - "hierarchical quantification of synergy in channels"

Connected through hyperedge HE-6 (induced by source paper "unique information and secret key agreement"):
  with members:
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "secret sharing and shared information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "on extractable shared information"
    - "quantifying unique information"
    - "bivariate measure of redundant information"

Connected through hyperedge HE-7 (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"
    - "extropy complementary dual of entropy"
    - "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-8 (induced by source paper "invariant components of synergy redundancy and unique information among three variables"):
  with members:
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "synergetic and redundant information flow detected by unnormalized granger causality application to resting state fmri"
    - "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems"
    - "quantifying unique information"
    - "intersection information based on common randomness"
    - "bivariate measure of redundant information"

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)