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: "scalable spectral algorithms for community detection in directed networks"
  Abstract: Community detection has been one of the central problems in network studies and directed network is particularly challenging due to asymmetry among its links. In this paper, we found that incorporating the direction of links reveals new perspectives on communities regarding to two different roles, source and terminal, that a node plays in each community. Intriguingly, such communities appear to be connected with unique spectral property of the graph Laplacian of the adjacency matrix and we exploit this connection by using regularized SVD methods. We propose harvesting algorithms, coupled with regularized SVDs, that are linearly scalable for efficient identification of communities in huge directed networks. The proposed algorithm shows great performance and scalability on benchmark networks in simulations and successfully recovers communities in real network applications.

Connected through hyperedge HE-1 (induced by source paper "detecting highly cyclic structure with complex eigenpairs"):
  with members:
    - "tensor spectral clustering for partitioning higher order network structures"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "using triangles to improve community detection in directed networks"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
    - "clustering and community detection in directed networks a survey"
    - "triadic measures on graphs the power of wedge sampling"
    - "directed closure measures for networks with reciprocity"

Connected through hyperedge HE-2 (induced by source paper "local community extraction in directed networks"):
  with members:
    - "a neurodynamic framework for local community extraction in networks"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-3 (induced by source paper "quantitative function and algorithm for community detection in bipartite networks"):
  with members:
    - "local community extraction in directed networks"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "maximizing barber s bipartite modularity is also hard"
    - "efficiently inferring community structure in bipartite networks"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "discovering link communities in complex networks by exploiting link dynamics"

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)