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: "an in depth analysis of stochastic kronecker graphs"
  Abstract: Graph analysis is playing an increasingly important role in science and industry. Due to numerous limitations in sharing real-world graphs, models for generating massive graphs are critical for developing better algorithms. In this article, we analyze the stochastic Kronecker graph model (SKG), which is the foundation of the Graph500 supercomputer benchmark due to its favorable properties and easy parallelization. Our goal is to provide a deeper understanding of the parameters and properties of this model so that its functionality as a benchmark is increased. We develop a rigorous mathematical analysis that shows this model cannot generate a power-law distribution or even a lognormal distribution. However, we formalize an enhanced version of the SKG model that uses random noise for smoothing. We prove both in theory and in practice that this enhancement leads to a lognormal distribution. Additionally, we provide a precise analysis of isolated vertices, showing that the graphs that are produced by SKG might be quite different than intended. For example, between 50p and 75p of the vertices in the Graph500 benchmarks will be isolated. Finally, we show that this model tends to produce extremely small core numbers (compared to most social networks and other real graphs) for common parameter choices.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "graph distance from the topological view of non backtracking cycles"):
  with members:
    - "an information theoretic all scales approach to comparing networks"
    - "on spectral graph embedding a non backtracking perspective and graph approximation"
    - "the metric space of networks"
    - "distances and isomorphism between networks and the stability of network invariants"
    - "localization and centrality in networks"
    - "deltacon a principled massive graph similarity function"
    - "spectral redemption in clustering sparse networks"
    - "network sampling from static to streaming graphs"

Connected through hyperedge HE-3 (induced by source paper "the hyperkron graph model for higher order features"):
  with members:
    - "higher order clustering in networks"
    - "the spacey random walk a stochastic process for higher order data"
    - "higher order organization of complex networks"
    - "a scalable generative graph model with community structure"
    - "social structure of facebook networks"

Connected through hyperedge HE-4 (induced by source paper "large graph models a review"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "a scalable generative graph model with community structure"
    - "using triangles to improve community detection in directed networks"
    - "external memory based distributed generation of massive scale social networks on small clusters"
    - "degree relations of triangles in real world networks and graph models"

Connected through hyperedge HE-5 (induced by source paper "modeling graphs using a mixture of kronecker models"):
  with members:
    - "a new algorithm for extracting a small representative subgraph from a very large graph"
    - "community structure and scale free collections of erdős renyi graphs"
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-6 (induced by source paper "a scalable generative graph model with community structure"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "decompositions of triangle dense graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "community structure and scale free collections of erdős renyi graphs"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"
    - "revisiting degree distribution models for social graph analysis"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "learning multifractal structure in large networks"):
  with members:
    - "community structure and scale free collections of erdős renyi graphs"
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-8 (induced by source paper "network sampling from static to streaming graphs"):
  with members:
    - "the role of social networks in information diffusion"
    - "space efficient sampling from social activity streams"
    - "towards unbiased bfs sampling"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "a comparison of techniques for sampling web pages"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

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)