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: "pointwise partial information decomposition using the specificity and ambiguity lattices"
  Abstract: What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.

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"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "quantifying synergistic information using intermediate stochastic variables"

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"
    - "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"
    - "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"

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"
    - "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"
    - "synergy redundancy and common information"

Connected through hyperedge HE-4 (induced by source paper "information decomposition based on cooperative game theory"):
  with members:
    - "a novel approach to multivariate redundancy and synergy"
    - "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"
    - "quantifying unique information"

Connected through hyperedge HE-5 (induced by source paper "quantifying high order interdependencies via multivariate extensions of the mutual information"):
  with members:
    - "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"
    - "quantifying synergistic mutual information"
    - "anatomy of a bit information in a time series observation"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-6 (induced by source paper "properties of unique information"):
  with members:
    - "computing the unique information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "bivariate measure of redundant information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-7 (induced by source paper "unique information and secret key agreement"):
  with members:
    - "secret sharing and shared information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "on extractable shared information"
    - "understanding interdependency through complex information sharing"
    - "quantifying unique information"
    - "bivariate measure of redundant information"

Connected through hyperedge HE-8 (induced by source paper "probability mass exclusions and the directed components of mutual information"):
  with members:
    - "bivariate measure of redundant information"
    - "shared information new insights and problems in decomposing information in 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)