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: "discovering link communities in complex networks by exploiting link dynamics"
  Abstract: Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links in networks. From this new perspective, we propose a link dynamics based algorithm, called UELC, for identifying link communities of networks. In UELC, the stochastic process of a link?node?link random walk is employed to unfold an embedded bipartition structure of links in a network. The local mixing properties of the Markov chain underlying the random walk are then utilized to extract two emerging  link communities. Further, the random walk and the bipartitioning processes are wrapped in an iterative subdivision strategy to recursively identify link partitions that segregate the network links into multiple subdivisions. We evaluate the performance of the new method on synthetic benchmarks and demonstrate its utility on real-world networks. Our experimental results show that our method is highly effective for discovering link communities in complex networks. As a comparison, we also extend UELC to extracting communities of nodes, and show that it is effective for node community identification.

Connected through hyperedge HE-1 (induced by source paper "permanence and community structure in complex networks"):
  with members:
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "a new metric for quality of network community structure"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "community detection and graph partitioning"
    - "enhancing community detection using a network weighting strategy"
    - "towards linear time overlapping community detection in social networks"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (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"
    - "scalable spectral algorithms for community detection in directed networks"

Connected through hyperedge HE-3 (induced by source paper "identification of hybrid node and link communities in complex networks"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "a classification for community discovery methods in complex networks"
    - "efficient and principled method for detecting communities in networks"
    - "empirical comparison of algorithms for network community detection"

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)