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: "on extractable shared information"
  Abstract: We consider the problem of quantifying the information shared by a pair of random variables      X 1  ,  X 2      about another variable S. We propose a new measure of shared information, called extractable shared information, that is left monotonic; that is, the information shared about S is bounded from below by the information shared about     f ( S )     for any function f. We show that our measure leads to a new nonnegative decomposition of the mutual information     I ( S ;  X 1   X 2  )     into shared, complementary and unique components. We study properties of this decomposition and show that a left monotonic shared information is not compatible with a Blackwell interpretation of unique information. We also discuss whether it is possible to have a decomposition in which both shared and unique information are left monotonic.

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 "understanding individual neuron importance using information theory"):
  with members:
    - "distilling critical paths in convolutional neural networks"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "revisiting the importance of individual units in cnns via ablation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "ablation of a robot s brain neural networks under a knife"
    - "estimating information flow in neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-3 (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"
    - "coarse graining and the blackwell order"
    - "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-4 (induced by source paper "unique information and secret key agreement"):
  with members:
    - "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"
    - "understanding interdependency through complex information sharing"
    - "quantifying unique information"
    - "bivariate measure of redundant information"

Connected through hyperedge HE-5 (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"
    - "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"
    - "some new insights into information decomposition in complex systems based on common information"
    - "bivariate measure of redundant information"

Connected through hyperedge HE-6 (induced by source paper "coarse graining and the blackwell order"):
  with members:
    - "quantifying unique 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)