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: "a scalable generative graph model with community structure"
  Abstract: Network data is ubiquitous and growing, yet we lack realistic generative network models that can be calibrated to match real-world data. The recently proposed block two-level Erdos--Renyi (BTER) model can be tuned to capture two fundamental properties: degree distribution and clustering coefficients. The latter is particularly important for reproducing graphs with community structure, such as social networks. In this paper, we compare BTER to other scalable models and show that it gives a better fit to real data. We provide a scalable implementation that requires only $O(d_{\rm max})$ storage, where $d_{\rm max}$ is the maximum number of neighbors for a single node. The generator is trivially parallelizable, and we show results for a Hadoop MapReduce implementation for modeling a real-world Web graph with over 4.6 billion edges. We propose that the BTER model can be used as a graph generator for benchmarking purposes and provide idealized degree distributions and clustering coefficient profiles that can b...

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 generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "gmark schema driven generation of graphs and queries"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"

Connected through hyperedge HE-3 (induced by source paper "artificial benchmark for community detection abcd fast random graph model with community structure"):
  with members:
    - "community structure a comparative evaluation of community detection methods"
    - "the switch markov chain for sampling irregular graphs and digraphs"
    - "communication free massively distributed graph generation"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "julia a fresh approach to numerical computing"
    - "are we there yet when to stop a markov chain while generating random graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-4 (induced by source paper "node immunization with non backtracking eigenvalues"):
  with members:
    - "graph comparison via the nonbacktracking spectrum"
    - "assessing percolation threshold based on high order non backtracking matrices"
    - "localization and centrality in networks"
    - "spectral redemption in clustering sparse networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "modeling graphs with vertex replacement grammars"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "learning hyperedge replacement grammars for graph generation"
    - "node2vec scalable feature learning for networks"
    - "growing graphs from hyperedge replacement graph grammars"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-6 (induced by source paper "machine learning in network centrality measures tutorial and outlook"):
  with members:
    - "an analysis of centrality measures for complex and social networks"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "computing classic closeness centrality at scale"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "representation learning a review and new perspectives"
    - "community structure and scale free collections of erdős renyi graphs"
    - "defining and evaluating network communities based on ground truth"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-7 (induced by source paper "publishing community preserving attributed social graphs with a differential privacy guarantee"):
  with members:
    - "robust active attacks on social graphs"
    - "community detection through likelihood optimization in search of a sound model"
    - "using node centrality and optimal control to maximize information diffusion in social networks"
    - "detecting communities under differential privacy"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "community detection in networks with node attributes"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "preserving link privacy in social network based systems"

Connected through hyperedge HE-8 (induced by source paper "an unsupervised framework for comparing graph embeddings"):
  with members:
    - "ensemble clustering for graphs comparisons and applications"
    - "verse versatile graph embeddings from similarity measures"
    - "ensemble clustering for graphs"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "community structure and scale free collections of erdős renyi graphs"

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)