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: "multivariate dependence beyond shannon information"
  Abstract: Accurately determining dependency structure is critical to understanding a complex system’s organization. We recently showed that the transfer entropy fails in a key aspect of this—measuring information flow—due to its conflation of dyadic and polyadic relationships. We extend this observation to demonstrate that Shannon information measures (entropy and mutual information, in their conditional and multivariate forms) can fail to accurately ascertain multivariate dependencies due to their conflation of qualitatively different relations among variables. This has broad implications, particularly when employing information to express the organization and mechanisms embedded in complex systems, including the burgeoning efforts to combine complex network theory with information theory. Here, we do not suggest that any aspect of information theory is wrong. Rather, the vast majority of its informational measures are simply inadequate for determining the meaningful relationships among variables within joint probability distributions. We close by demonstrating that such distributions exist across an arbitrary set of variables.

Connected through hyperedge HE-1 (induced by source paper "a partial information decomposition based on causal tensors"):
  with members:
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "shared information new insights and problems in decomposing information in complex systems"
    - "nonnegative decomposition of multivariate information"

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 "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"
    - "geometry of information integration"
    - "understanding interdependency through complex information sharing"
    - "hierarchical quantification of synergy in channels"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"

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"
    - "understanding interdependency through complex information sharing"
    - "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 "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"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-6 (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"
    - "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"
    - "hierarchical quantification of synergy in channels"

Connected through hyperedge HE-7 (induced by source paper "multivariate extension of matrix based renyi s alpha order entropy functional"):
  with members:
    - "feature selection a data perspective"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"
    - "measures of entropy from data using infinitely divisible kernels"
    - "multivariate information measures an experimentalist s perspective"
    - "nonnegative decomposition of multivariate information"
    - "quantifying and visualizing attribute interactions"

Connected through hyperedge HE-8 (induced by source paper "assessing information transmission in data transformations with the channel multivariate entropy triangle"):
  with members:
    - "anatomy of a bit information in a time series observation"

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)