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: "quantifying and visualizing attribute interactions"
  Abstract: Interactions are patterns between several attributes in data that cannot be inferred from any subset of these attributes. While mutual information is a well-established approach to evaluating the interactions between two attributes, we surveyed its generalizations as to quantify interactions between several attributes. We have chosen McGill's interaction information, which has been independently rediscovered a number of times under various names in various disciplines, because of its many intuitively appealing properties. We apply interaction information to visually present the most important interactions of the data. Visualization of interactions has provided insight into the structure of data on a number of domains, identifying redundant attributes and opportunities for constructing new features, discovering unexpected regularities in data, and have helped during construction of predictive models; we illustrate the methods on numerous examples. A machine learning method that disregards interactions may get caught in two traps: myopia is caused by learning algorithms assuming independence in spite of interactions, whereas fragmentation arises from assuming an interaction in spite of independence.

Connected through hyperedge HE-1 (induced by source paper "multivariate extension of matrix based renyi s alpha order entropy functional"):
  with members:
    - "feature selection a data perspective"
    - "multivariate dependence beyond shannon information"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"
    - "measures of entropy from data using infinitely divisible kernels"
    - "multivariate information measures an experimentalist s perspective"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-2 (induced by source paper "measuring multivariate redundant information with pointwise common change in surprisal"):
  with members:
    - "secret sharing and shared information"
    - "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"
    - "multivariate dependence beyond shannon 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"
    - "quantifying synergistic mutual information"
    - "intersection information based on common randomness"

Connected through hyperedge HE-3 (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"
    - "some new insights into information decomposition in complex systems based on common 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.AI(Artificial Intelligence)