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: "coarse graining and the blackwell order"
  Abstract: Suppose we have a pair of information channels,      κ 1  ,  κ 2     , with a common input. The Blackwell order is a partial order over channels that compares     κ 1     and     κ 2     by the maximal expected utility an agent can obtain when decisions are based on the channel outputs. Equivalently,     κ 1     is said to be Blackwell-inferior to     κ 2     if and only if     κ 1     can be constructed by garbling the output of     κ 2    . A related partial order stipulates that     κ 2     is more capable than     κ 1     if the mutual information between the input and output is larger for     κ 2     than for     κ 1     for any distribution over inputs. A Blackwell-inferior channel is necessarily less capable. However, examples are known where     κ 1     is less capable than     κ 2     but not Blackwell-inferior. We show that this may even happen when     κ 1     is constructed by coarse-graining the inputs of     κ 2    . Such a coarse-graining is a special kind of “pre-garbling” of the channel inputs. This example directly establishes that the expected value of the shared utility function for the coarse-grained channel is larger than it is for the non-coarse-grained channel. This contradicts the intuition that coarse-graining can only destroy information and lead to inferior channels. We also discuss our results in the context of information decompositions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "pointwise partial information decomposition using the specificity and ambiguity lattices"):
  with members:
    - "invariant components of synergy redundancy and unique information among three variables"
    - "secret sharing and shared information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "multivariate dependence beyond shannon information"
    - "on extractable shared information"
    - "understanding interdependency through complex information sharing"
    - "synergetic and redundant information flow detected by unnormalized granger causality application to resting state fmri"
    - "hierarchical quantification of synergy in channels"

Connected through hyperedge HE-3 (induced by source paper "adaptive polling in hierarchical social networks using blackwell dominance"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "active sequential hypothesis testing"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

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)