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 "pointwise partial information decomposition using the specificity and ambiguity lattices"):
  Abstract: What are the distinct ways in which a set of predictor variables can provide information about a target variable? When does a variable provide unique information, when do variables share redundant information, and when do variables combine synergistically to provide complementary information? The redundancy lattice from the partial information decomposition of Williams and Beer provided a promising glimpse at the answer to these questions. However, this structure was constructed using a much criticised measure of redundant information, and despite sustained research, no completely satisfactory replacement measure has been proposed. In this paper, we take a different approach, applying the axiomatic derivation of the redundancy lattice to a single realisation from a set of discrete variables. To overcome the difficulty associated with signed pointwise mutual information, we apply this decomposition separately to the unsigned entropic components of pointwise mutual information which we refer to as the specificity and ambiguity. This yields a separate redundancy lattice for each component. Then based upon an operational interpretation of redundancy, we define measures of redundant specificity and ambiguity enabling us to evaluate the partial information atoms in each lattice. These atoms can be recombined to yield the sought-after multivariate information decomposition. We apply this framework to canonical examples from the literature and discuss the results and the various properties of the decomposition. In particular, the pointwise decomposition using specificity and ambiguity satisfies a chain rule over target variables, which provides new insights into the so-called two-bit-copy example.

Includes members:
  Member "invariant components of synergy redundancy and unique information among three variables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalised measures of multivariate information content")
  Member "secret sharing and shared information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalised measures of multivariate information content")
  Member "measuring multivariate redundant information with pointwise common change in surprisal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "multivariate dependence beyond shannon information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a partial information decomposition based on causal tensors")
  Member "on extractable shared information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalised measures of multivariate information content")
  Member "coarse graining and the blackwell order":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalised measures of multivariate information content")
  Member "understanding interdependency through complex information sharing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalised measures of multivariate information content")
  Member "synergetic and redundant information flow detected by unnormalized granger causality application to resting state fmri":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "invariant components of synergy redundancy and unique information among three variables")

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)