human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "krylov subspace approximation for local community detection in large networks"):
  Abstract: Community detection is an important information mining task to uncover modular structures in large networks. For increasingly common large network data sets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities, i.e. identifying all latent members of a particular community from a few labeled seed members. To address such semi-supervised mining task, we systematically develop a local spectral subspace-based community detection method, called LOSP. We define a family of local spectral subspaces based on Krylov subspaces, and seek a sparse indicator for the target community via an $\ell_1$ norm minimization over the Krylov subspace. Variants of LOSP depend on type of random walks with different diffusion speeds, type of random walks, dimension of the local spectral subspace and step of diffusions. The effectiveness of the proposed LOSP approach is theoretically analyzed based on Rayleigh quotients, and it is experimentally verified on a wide variety of real-world networks across social, production and biological domains, as well as on an extensive set of synthetic LFR benchmark datasets.

Includes members:
  Member "overlapping community detection using superior seed set selection in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "krylov subspace approximation for local community detection")
  Member "hidden community detection in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hidden community detection on two layer stochastic models a theoretical prospective")
  Member "local network community detection with continuous optimization of conductance and weighted kernel k means":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "correlation based community detection")
  Member "think locally act locally detection of small medium sized and large communities in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "detecting overlapping communities from local spectral subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "uncovering the small community structure in large networks a local spectral approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "overlapping community detection using neighborhood inflated seed expansion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "community detection in node attributed social networks a survey")
  Member "heat kernel based community detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")

Question: Please tell me which class the source paper 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)