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: "computing the unique information"
  Abstract: Given a pair of predictor variables and a response variable, how much information do the predictors have about the response, and how is this information distributed between unique, redundant, and synergistic components? Recent work has proposed to quantify the unique component of the decomposition as the minimum value of the conditional mutual information over a constrained set of information channels. We present an efficient iterative divergence minimization algorithm to solve this optimization problem with convergence guarantees and evaluate its performance against other techniques. A full version of this paper is accessible at: https://arxiv.org/abs/1709.07487

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"
    - "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"
    - "quantifying synergistic information using intermediate stochastic variables"

Connected through hyperedge HE-2 (induced by source paper "unique information and secret key decompositions"):
  with members:
    - "unique informations and deficiencies"
    - "coding for positive rate in the source model key agreement problem"
    - "the blackwell relation defines no lattice"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-3 (induced by source paper "properties of unique information"):
  with members:
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "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-4 (induced by source paper "unique informations and deficiencies"):
  with members:
    - "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"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "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)