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: "graph spectra and the detectability of community structure in networks"
  Abstract: We study networks that display community structure -- groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.

Connected through hyperedge HE-1 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure"):
  with members:
    - "graph powering and spectral robustness"
    - "robustness of spectral methods for community detection"
    - "detectability of the spectral method for sparse graph partitioning"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "spectral redemption in clustering sparse networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for task driven discovery of incomplete networks"):
  with members:
    - "partitioning graphs for the cloud using reinforcement learning"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep learning on graphs a survey"
    - "gemsec graph embedding with self clustering"
    - "selective harvesting over networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-3 (induced by source paper "modularity belief propagation on multilayer networks to detect significant community structure"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "post processing partitions to identify domains of modularity optimization"
    - "the many facets of community detection in complex networks"
    - "the ground truth about metadata and community detection in networks"

Connected through hyperedge HE-4 (induced by source paper "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"):
  with members:
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"):
  with members:
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "spectra of networks containing short loops"):
  with members:
    - "localization and centrality in networks"
    - "localization on low order eigenvectors of data matrices"

Connected through hyperedge HE-7 (induced by source paper "community detectability and structural balance dynamics in signed networks"):
  with members:
    - "balance in signed networks"
    - "spectra of random graphs with community structure and arbitrary degrees"

Connected through hyperedge HE-8 (induced by source paper "modularity and projection of bipartite networks"):
  with members:
    - "the ground truth about metadata and community detection in networks"

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)