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: "community structure and scale free collections of erdős renyi graphs"
  Abstract: Community structure plays a significant role in the analysis of social networks and similar graphs, yet this structure is little understood and not well captured by most models. We formally define a community to be a subgraph that is internally highly connected and has no deeper substructure. We use tools of combinatorics to show that any such community must contain a dense Erd˝ os-R´ enyi (ER) subgraph. Based on mathematical arguments, we hypothesize that any graph with a heavy-tailed degree distribution and community structure must contain a scale-free collection of dense ER subgraphs. These theoretical observations corroborate well with empirical evidence. From this, we propose the Block Two-Level Erd˝ os-R´ enyi (BTER) model, and demonstrate that it accurately captures the observable properties of many real-world social networks.

Connected through hyperedge HE-1 (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"
    - "a scalable generative graph model with community structure"
    - "are we there yet when to stop a markov chain while generating random graphs"

Connected through hyperedge HE-2 (induced by source paper "understanding the limitations of network online learning"):
  with members:
    - "selective harvesting over networks"
    - "node2vec scalable feature learning for networks"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "loss minimization and parameter estimation with heavy tails"
    - "network sampling from static to streaming graphs"

Connected through hyperedge HE-3 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "maximizing social influence in nearly optimal time"
    - "information theoretic measures of influence based on content dynamics"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "a scalable generative graph model with community structure"
    - "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"
    - "defining and evaluating network communities based on ground truth"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-5 (induced by source paper "towards interpretable graph modeling with vertex replacement grammars"):
  with members:
    - "edge replacement grammars a formal language approach for generating graphs"
    - "modeling graphs with vertex replacement grammars"
    - "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"
    - "motifs in temporal networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "influence maximization for social good use of social networks in low resource communities"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "information theoretic measures of influence based on content dynamics"
    - "social contagion an empirical study of information spread on digg and twitter follower graphs"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "community detection using a measure of global influence"

Connected through hyperedge HE-7 (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"
    - "a scalable generative graph model with community structure"

Connected through hyperedge HE-8 (induced by source paper "measuring directed triadic closure with closure coefficients"):
  with members:
    - "the switch markov chain for sampling irregular graphs"
    - "directed closure measures for networks with reciprocity"
    - "friendship networks and social status"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)