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: "efficient and principled method for detecting communities in networks"
  Abstract: A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.

Connected through hyperedge HE-1 (induced by source paper "exact blind community detection from signals on multiple graphs"):
  with members:
    - "network inference from consensus dynamics with unknown parameters"
    - "blind inference of centrality rankings from graph signals"
    - "blind identification of stochastic block models from dynamical observations"
    - "blind community detection from low rank excitations of a graph filter"
    - "blind identification of graph filters"
    - "network inference from consensus dynamics"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-2 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks"):
  with members:
    - "gemsec graph embedding with self clustering"
    - "hidden community detection in social networks"
    - "the ground truth about metadata and community detection in networks"
    - "community detection in networks with unequal groups"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "spectral redemption in clustering sparse networks"
    - "demon a local first discovery method for overlapping communities"
    - "percolation computation in complex networks"

Connected through hyperedge HE-3 (induced by source paper "discovering communities of community discovery"):
  with members:
    - "a survey of community search over big graphs"
    - "community structure a comparative evaluation of community detection methods"
    - "community discovery in dynamic networks a survey"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"
    - "demon a local first discovery method for overlapping communities"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial enhancement for community detection in complex networks"):
  with members:
    - "edmot an edge enhancement approach for motif aware community detection"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "ga based q attack on community detection"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "node2vec scalable feature learning for networks"
    - "enhancing community detection using a network weighting strategy"

Connected through hyperedge HE-5 (induced by source paper "latent channel networks"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "julia a fresh approach to numerical computing"
    - "exact recovery in the stochastic block model"
    - "social structure of facebook networks"

Connected through hyperedge HE-6 (induced by source paper "unsupervised community detection with modularity based attention model"):
  with members:
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-7 (induced by source paper "algorithmic detectability threshold of the stochastic block model"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "structure and inference in annotated networks"
    - "estimating the number of communities in a network"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"

Connected through hyperedge HE-8 (induced by source paper "prioritizing network communities"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "a tensor approach to learning mixed membership community models"
    - "distributed representations of words and phrases and their compositionality"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"

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)