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: "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems"
  Abstract: To fully characterize the information that two source variables carry about a third target variable, one must decompose the total information into redundant, unique, and synergistic components, i.e., obtain a partial information decomposition (PID). However, Shannon's theory of information does not provide formulas to fully determine these quantities. Several recent studies have begun addressing this. Some possible definitions for PID quantities have been proposed and some analyses have been carried out on systems composed of discrete variables. Here we present an in-depth analysis of PIDs on Gaussian systems, both static and dynamical. We show that, for a broad class of Gaussian systems, previously proposed PID formulas imply that (i) redundancy reduces to the minimum information provided by either source variable and hence is independent of correlation between sources, and (ii) synergy is the extra information contributed by the weaker source when the stronger source is known and can either increase or decrease with correlation between sources. We find that Gaussian systems frequently exhibit net synergy, i.e., the information carried jointly by both sources is greater than the sum of information carried by each source individually. Drawing from several explicit examples, we discuss the implications of these findings for measures of information transfer and information-based measures of complexity, both generally and within a neuroscience setting. Importantly, by providing independent formulas for synergy and redundancy applicable to continuous time-series data, we provide an approach to characterizing and quantifying information sharing amongst complex system variables.

Connected through hyperedge HE-1 (induced by source paper "an operational information decomposition via synergistic disclosure"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "a novel approach to multivariate redundancy and synergy"
    - "information decomposition based on cooperative game theory"
    - "quantifying high order interdependencies via multivariate extensions of the mutual information"
    - "computing the unique information"
    - "unique information and secret key agreement"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "measuring multivariate redundant information with pointwise common change in surprisal"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a novel approach to multivariate redundancy and synergy"):
  with members:
    - "understanding individual neuron importance using information theory"
    - "unique information and secret key agreement"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "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"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "on the input degradedness and input equivalence between channels"

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"
    - "understanding interdependency through complex information sharing"
    - "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"
    - "understanding interdependency through complex information sharing"
    - "synergetic and redundant information flow detected by unnormalized granger causality application to resting state fmri"

Connected through hyperedge HE-6 (induced by source paper "measuring multivariate redundant information with pointwise common change in surprisal"):
  with members:
    - "secret sharing and shared information"
    - "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"
    - "multivariate dependence beyond shannon information"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "intersection information based on common randomness"
    - "quantifying redundant information in predicting a target random variable"

Connected through hyperedge HE-7 (induced by source paper "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"):
  with members:
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "multivariate dependence beyond shannon information"
    - "on extractable shared information"
    - "synergy redundancy and common information"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "some new insights into information decomposition in complex systems based on common information"
    - "bivariate measure of redundant information"

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"
    - "understanding interdependency through complex information sharing"
    - "synergetic and redundant information flow detected by unnormalized granger causality application to resting state fmri"
    - "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)