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 detection in networks via nonlinear modularity eigenvectors"
  Abstract: Revealing a community structure in a network or dataset is a central problem arising in many scientific areas. The modularity function $Q$ is an established measure quantifying the quality of a com...

Connected through hyperedge HE-1 (induced by source paper "stochastic block models are a discrete surface tension"):
  with members:
    - "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "efficient method for estimating the number of communities in a network"
    - "block models and personalized pagerank"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-2 (induced by source paper "total variation based community detection using a nonlinear optimization approach"):
  with members:
    - "a nonlinear spectral method for core periphery detection in networks"
    - "community detection in political twitter networks using nonnegative matrix factorization methods"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "advanced coarsening schemes for graph partitioning"
    - "learning with submodular functions a convex optimization perspective"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"

Connected through hyperedge HE-3 (induced by source paper "node classification for signed networks using diffuse interface methods"):
  with members:
    - "spectral clustering of signed graphs via matrix power means"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "on spectral partitioning of signed graphs"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"
    - "a survey of signed network mining in social media"
    - "vews a wikipedia vandal early warning system"

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)