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: "quantifying synergistic mutual information"
  Abstract: Synergy is a fundamental concept in complex systems that has received much attention in computational biology (Narayanan et al. 2005; Balduzzi and Tononi 2008). Several papers (Schneidman et al. 2003a; Bell 2003; Nirenberg et al. 2001;Williams and Beer 2010) have proposed measures for quantifying synergy, but there remains no consensus which measure is most valid.

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 "a differentiable measure of pointwise shared information"):
  with members:
    - "probability mass exclusions and the directed components of mutual information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "quantifying unique information"
    - "bivariate measure of redundant information"
    - "shared information new insights and problems in decomposing information in complex systems"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "algorithmic networks central time to trigger expected emergent open endedness"):
  with members:
    - "self referential basis of undecidable dynamics from the liar paradox and the halting problem to the edge of chaos"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "computing halting probabilities from other halting probabilities"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "relativizing an incompressible number and an incompressible function through subrecursive extensions of turing machines"
    - "on multiaspect graphs"

Connected through hyperedge HE-6 (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"
    - "understanding interdependency through complex information sharing"
    - "hierarchical quantification of synergy in channels"
    - "reconsidering unique information towards a multivariate information decomposition"

Connected through hyperedge HE-7 (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"
    - "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems"
    - "anatomy of a bit information in a time series observation"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-8 (induced by source paper "infotheory a c python package for multivariate information theoretic analysis"):
  with members:
    - "idtxl the information dynamics toolkit xl a python package for the efficient analysis of multivariate information dynamics in networks"
    - "multivariate dependence beyond shannon information"
    - "quantifying unique information"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "nonnegative decomposition of multivariate 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)