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: "reconsidering unique information towards a multivariate information decomposition"
  Abstract: The information that two random variables $Y$, $Z$ contain about a third random variable $X$ can have aspects of shared information (contained in both $Y$ and $Z$), of complementary information (only available from $(Y,Z)$ together) and of unique information (contained exclusively in either $Y$ or $Z$). Here, we study measures $\widetilde{SI}$ of shared, $\widetilde{UI}$ unique and $\widetilde{CI}$ complementary information introduced by Bertschinger et al., which are motivated from a decision theoretic perspective. We find that in most cases the intuitive rule that more variables contain more information applies, with the exception that $\widetilde{SI}$ and $\widetilde{CI}$ information are not monotone in the target variable $X$. Additionally, we show that it is not possible to extend the bivariate information decomposition into $\widetilde{SI}$, $\widetilde{UI}$ and $\widetilde{CI}$ to a non-negative decomposition on the partial information lattice of Williams and Beer. Nevertheless, the quantities $\widetilde{UI}$, $\widetilde{SI}$ and $\widetilde{CI}$ have a well-defined interpretation, even in the multivariate setting.

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 partial information decomposition based on causal tensors"):
  with members:
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "multivariate dependence beyond shannon information"
    - "quantifying unique 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 "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"
    - "quantifying unique information"

Connected through hyperedge HE-6 (induced by source paper "unique information and secret key decompositions"):
  with members:
    - "unique informations and deficiencies"
    - "computing the unique information"
    - "coding for positive rate in the source model key agreement problem"
    - "the blackwell relation defines no lattice"
    - "quantifying unique information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-7 (induced by source paper "properties of unique information"):
  with members:
    - "computing the unique information"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "bivariate measure of redundant information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-8 (induced by source paper "unique informations and deficiencies"):
  with members:
    - "computing the unique information"
    - "secret sharing and shared information"
    - "disentangled representations via synergy minimization"
    - "on the input degradedness and input equivalence between channels"
    - "the blackwell relation defines no lattice"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "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)