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: "uncovering the small community structure in large networks a local spectral approach"
  Abstract: Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure when dealing with large networks. A growing body of work has been adopting local expansion methods in order to identify the community from a few exemplary seed members. #R##N#In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). Different from PageRank-like diffusion methods, LEMON finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph. The algorithm is easy to implement, and is highly parallelizable. #R##N#Moreover, given that networks are not all similar in nature, a comprehensive analysis on how the local expansion approach is suited for uncovering communities in different networks is still lacking. We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the quality and quantity of the seed set would affect the performance are provided.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering"):
  with members:
    - "statistical guarantees for local graph clustering"
    - "power weighted shortest paths for clustering euclidean data"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "a simple and strongly local flow based method for cut improvement"
    - "detecting overlapping communities from local spectral subspaces"
    - "computing heat kernel pagerank and a local clustering algorithm"

Connected through hyperedge HE-2 (induced by source paper "optimizing generalized pagerank methods for seed expansion community detection"):
  with members:
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "quadratic decomposable submodular function minimization theory and practice"
    - "diffusion improves graph learning"
    - "mean field analysis of personalized pagerank with implications for local graph clustering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "block models and personalized pagerank"
    - "snap a general purpose network analysis and graph mining library"

Connected through hyperedge HE-3 (induced by source paper "overlapping community detection with graph neural networks"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "verse versatile graph embeddings from similarity measures"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "high quality disjoint and overlapping community structure in large scale complex networks"):
  with members:
    - "dynamic structural similarity on graphs"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "improving pagerank for local community detection"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "heat kernel based community detection"
    - "community detection via local dynamic interaction"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-5 (induced by source paper "krylov subspace approximation for local community detection in large networks"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "hidden community detection in social networks"
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "detecting overlapping communities from local spectral subspaces"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-6 (induced by source paper "iedc an integrated approach for overlapping and non overlapping community detection"):
  with members:
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "relevant based structure learning for feature selection"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "general optimization technique for high quality community detection in complex networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "social structure of facebook networks"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "in a world that counts clustering and detecting fake social engagement at scale"):
  with members:
    - "overlapping community detection via local spectral clustering"
    - "heat kernel based community detection"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "network analysis of recurring youtube spam campaigns"
    - "finding endogenously formed communities"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-8 (induced by source paper "scalable and robust local community detection via adaptive subgraph extraction and diffusions"):
  with members:
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "overlapping community detection via local spectral clustering"
    - "heat kernel based community detection"
    - "a dynamical system for pagerank with time dependent teleportation"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "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)