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: "geosocial graph based community detection"
  Abstract: We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops involving street gang members in the LAPD district of Hollenbeck. We then compare the algorithmically detected communities with known gang identifications and argue that discrepancies are due to sparsity of social connections in the data as well as complex underlying sociological factors that blur distinctions between communities.

Connected through hyperedge HE-1 (induced by source paper "phase transitions in spectral community detection of large noisy networks"):
  with members:
    - "universal phase transition in community detectability under a stochastic block model"
    - "deep community detection"
    - "phase transitions in spectral community detection"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral redemption in clustering sparse networks"
    - "discrete signal processing on graphs"
    - "graph spectra and the detectability of community structure in networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "phase transitions in spectral community detection"):
  with members:
    - "universal phase transition in community detectability under a stochastic block model"
    - "node removal vulnerability of the largest component of a network"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral redemption in clustering sparse networks"
    - "discrete signal processing on graphs"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "graph spectra and the detectability of community structure in networks"
    - "a tutorial on spectral clustering"

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)