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: "detecting overlapping communities from local spectral subspaces"
  Abstract: Based on the definition of local spectral subspace, we propose a novel approach called LOSP for local overlapping community detection. Instead of using the invariant subspace spanned by the dominant eigenvectors of the entire network, we run the power method for a few steps to approximate the leading eigenvectors that depict the embedding of the local neighborhood structure around seeds of interest. We then seek a sparse approximate indicator vector in the local spectral subspace spanned by these vectors such that the seeds are in its support. #R##N#We evaluate LOSP on five large real world networks across various domains with labeled ground-truth communities and compare the results with the state-of-the-art community detection approaches. LOSP identifies the members of a target community with high accuracy from very few seed members, and outperforms the local Heat Kernel or PageRank diffusions as well as the global baselines. #R##N#Two candidate definitions of the local spectral subspace are analyzed, and different community scoring functions for determining the community boundary, including two new metrics, are thoroughly evaluated. The structural properties of different seed sets and the impact of the seed set size are discussed. We observe low degree seeds behave better, and LOSP is robust even when started from a single random seed. #R##N#Using LOSP as a subroutine and starting from each ego connected component, we try the harder yet significant task of identifying all communities a single vertex is in. Experiments show that the proposed method achieves high F1 measures on the detected multiple local overlapping communities containing the seed vertex.

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"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "uncovering the small community structure in large networks a local spectral approach"

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 "megan a generative adversarial network for multi view network embedding"):
  with members:
    - "an attention based collaboration framework for multi view network representation learning"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition"):
  with members:
    - "tensor spectral clustering for partitioning higher order network structures"
    - "a tensor approach to learning mixed membership community models"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "online tensor methods for learning latent variable models"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "community detection in networks with node attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

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"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "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 "krylov subspace approximation for local community detection"):
  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"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "hidden community detection in social networks"):
  with members:
    - "revealing multiple layers of hidden community structure in networks"
    - "deep community detection"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "social structure of facebook networks"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-8 (induced by source paper "overlapping community detection via local spectral clustering"):
  with members:
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "a markov random walk under constraint for discovering overlapping communities in complex networks"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"

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)