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: "some new insights into information decomposition in complex systems based on common information"
  Abstract: We take a closer look at the structure of bivariate dependency induced by a pair of predictor random variables (X,Y) trying to encode a target random variable, Z. The information that the pair (X,Y) contains about the target Z can have aspects of redundant information (contained identically in both X and Y), of unique information (contained exclusively in either X or Y), and of synergistic information (available when (X,Y) are together). Williams and Beer proposed such a decomposition for the general case of K predictors to specify how the total information about the target is shared across the singleton predictors and their overlapping or disjoint coalitions. However, effecting a non-negative decomposition is known to be a surprisingly difficult problem even for the case of K = 3. In particular, it is not always possible to attribute operational significance to all the atoms induced by the decomposition. What operational questions should an ideal measure of redundant or unique information answer? In this paper, we seek to demonstrate the richness of this question through the lens of network information theory. We show the following: We evaluate a recently proposed measure of redundancy based on the Gacs and Korner's common information (Griffith et al., Entropy, vol. 16, no. 4, pp. 1985–2000, 2014) and show that the measure, in spite of its elegance is degenerate for most non-trivial distributions. We show that Wyner's common information also fails to capture the notion of redundancy as it violates an intuitive monotonically non-increasing property. We further show why a combinatorial dual of the Gacs and Korner's CI is unremarkable as a measure of unique information. We identify a set of conditions when a conditional version of Gacs and Korner's common information is an ideal measure of unique information. Finally, we show how the notions of approximately sufficient statistics and conditional information bottleneck can be used to quantify unique information.

Connected through hyperedge HE-1 (induced by source paper "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"):
  with members:
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "multivariate dependence beyond shannon information"
    - "on extractable shared information"
    - "synergy redundancy and common information"
    - "exploration of synergistic and redundant information sharing in static and dynamical gaussian systems"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique 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)