human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "measuring multivariate redundant information with pointwise common change in surprisal"):
  Abstract: The problem of how to properly quantify redundant information is an open question that has been the subject of much recent research. Redundant information refers to information about a target variable S that is common to two or more predictor variables     X i    . It can be thought of as quantifying overlapping information content or similarities in the representation of S between the     X i    . We present a new measure of redundancy which measures the common change in surprisal shared between variables at the local or pointwise level. We provide a game-theoretic operational definition of unique information, and use this to derive constraints which are used to obtain a maximum entropy distribution. Redundancy is then calculated from this maximum entropy distribution by counting only those local co-information terms which admit an unambiguous interpretation as redundant information. We show how this redundancy measure can be used within the framework of the Partial Information Decomposition (PID) to give an intuitive decomposition of the multivariate mutual information into redundant, unique and synergistic contributions. We compare our new measure to existing approaches over a range of example systems, including continuous Gaussian variables. Matlab code for the measure is provided, including all considered examples.

Includes members:
  Member "secret sharing and shared information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalised measures of multivariate information content")
  Member "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalised measures of multivariate information content")
  Member "multivariate dependence beyond shannon information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a partial information decomposition based on causal tensors")
  Member "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "reconsidering unique information towards a multivariate information decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "quantifying unique information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "quantifying synergistic mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "intersection information based on common randomness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an operational information decomposition via synergistic disclosure")

Question: Please tell me which class the source paper 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)