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"):
  Abstract: Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics. 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. methods that identify all latent members of a particular community from a few labeled seed members. To address this semi-supervised mining task, we propose a local spectral subspace-based community detection method, called LOSP. A sampling technique is first applied around the seeds to significantly reduce the scale of the search. Then we define a family of local spectral subspaces based on Krylov subspaces, and seek a sparse indicator for the target community via an one norm minimization over the Krylov subspace. Variants of LOSP depend on types of random walks with different diffusion speeds, regular random walk or inverse random walk, dimension of the local spectral subspace and steps of diffusions. The effectiveness of the proposed LOSP approach is theoretically analyzed based on Rayleigh quotients, and 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 in large networks")
  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 "heat kernel based community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning with relational information for short sequences")
  Member "demon a local first discovery method for overlapping communities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks")

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)