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 information using intermediate stochastic variables"
  Abstract: Quantifying synergy among stochastic variables is an important open problem in information theory. Information synergy occurs when multiple sources together predict an outcome variable better than the sum of single-source predictions. It is an essential phenomenon in biology such as in neuronal networks and cellular regulatory processes, where different information flows integrate to produce a single response, but also in social cooperation processes as well as in statistical inference tasks in machine learning. Here we propose a metric of synergistic entropy and synergistic information from first principles. The proposed measure relies on so-called synergistic random variables (SRVs) which are constructed to have zero mutual information about individual source variables but non-zero mutual information about the complete set of source variables. We prove several basic and desired properties of our measure, including bounds and additivity properties. In addition, we prove several important consequences of our measure, including the fact that different types of synergistic information may co-exist between the same sets of variables. A numerical implementation is provided, which we use to demonstrate that synergy is associated with resilience to noise. Our measure may be a marked step forward in the study of multivariate information theory and its numerous applications.

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 "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 "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"
    - "on the input degradedness and input equivalence between channels"
    - "synergy redundancy and common information"

Connected through hyperedge HE-4 (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"
    - "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"
    - "formal definitions of unbounded evolution and innovation reveal universal mechanisms for open ended evolution in dynamical systems"

Connected through hyperedge HE-5 (induced by source paper "data disclosure under perfect sample privacy"):
  with members:
    - "quantifying high order interdependencies via multivariate extensions of the mutual information"
    - "generative adversarial privacy"
    - "on perfect privacy and maximal correlation"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "understanding interdependency through complex information sharing"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"

Connected through hyperedge HE-6 (induced by source paper "information processing features can detect behavioral regimes of dynamical systems"):
  with members:
    - "intersection information based on common randomness"
    - "quantifying redundant information in predicting a target random variable"
    - "towards a synergy based approach to measuring information modification"
    - "the diminishing role of hubs in dynamical processes on complex networks"
    - "anatomy of a bit information in a time series observation"
    - "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)