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: "stability and continuity of centrality measures in weighted graphs"
  Abstract: This paper presents a formal definition of stability for node centrality measures in weighted graphs. It is shown that the commonly used measures of degree, closeness and eigenvector centrality are stable whereas betweenness centrality is not. An alternative definition of the latter that preserves the same centrality notion while satisfying the stability criteria is introduced. Continuity is presented as a less stringent alternative to stability. Betweenness centrality is shown to be not only unstable but discontinuous. Numerical experiments in synthetic random networks and real-world data show that, in practice, stability and continuity imply different levels of robustness in the presence of noisy data. In particular, the stable betweenness centrality is shown to exhibit resilience against noise that is absent in the discontinuous and unstable standard betweenness centrality, while preserving a similar notion of centrality.

Connected through hyperedge HE-1 (induced by source paper "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures"):
  with members:
    - "blind inference of centrality rankings from graph signals"
    - "towards quantifying sampling bias in network inference"
    - "sampling from social networks with attributes"
    - "node2vec scalable feature learning for networks"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs"):
  with members:
    - "an intuitive derivation of the coherence index relation in compressive sensing"
    - "graph filters and the z laplacian"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "hilbert transform analytic signal and modulation analysis for graph signal processing"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "stationary time vertex signal processing"

Connected through hyperedge HE-3 (induced by source paper "modelling graph errors towards robust graph signal processing"):
  with members:
    - "filtering random graph processes over random time varying graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "learning sparse graphs under smoothness prior"
    - "sampling of graph signals with successive local aggregations"
    - "discrete signal processing on graphs sampling theory"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-4 (induced by source paper "blind inference of centrality rankings from graph signals"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "sampling on networks estimating eigenvector centrality on incomplete graphs"
    - "network inference from consensus dynamics with unknown parameters"
    - "spectral partitioning of time varying networks with unobserved edges"
    - "blind identification of stochastic block models from dynamical observations"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-5 (induced by source paper "on the role of words in the network structure of texts application to authorship attribution"):
  with members:
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "probing the topological properties of complex networks modeling short written texts"
    - "authorship attribution through function word adjacency networks"
    - "a systematic comparison of supervised classifiers"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-6 (induced by source paper "distributed algorithms for computation of centrality measures in complex networks"):
  with members:
    - "ergodic randomized algorithms and dynamics over networks"
    - "convergence of distributed randomized pagerank algorithms"
    - "axioms for centrality"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "distributed randomized algorithms for the pagerank computation"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"

Connected through hyperedge HE-7 (induced by source paper "centrality measures for graphons accounting for uncertainty in networks"):
  with members:
    - "graphons a nonparametric method to model estimate and design algorithms for massive networks"
    - "distributions of centrality on networks"
    - "pagerank beyond the web"
    - "centrality metric for dynamic networks"
    - "co authorship networks in the digital library research community"

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.SI(Social and Information Networks)