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: "overlapping communities in social networks"
  Abstract: Complex networks can be typically broken down into groups or modules. Discovering this "community structure" is an important step in studying the large-scale structure of networks. Many algorithms have been proposed for community detection and benchmarks have been created to evaluate their performance. Typically algorithms for community detection either partition the graph (non-overlapping communities) or find node covers (overlapping communities). #R##N#In this paper, we propose a particularly simple semi-supervised learning algorithm for finding out communities. In essence, given the community information of a small number of "seed nodes", the method uses random walks from the seed nodes to uncover the community information of the whole network. The algorithm runs in time $O(k \cdot m \cdot \log n)$, where $m$ is the number of edges; $n$ the number of links; and $k$ the number of communities in the network. In sparse networks with $m = O(n)$ and a constant number of communities, this running time is almost linear in the size of the network. Another important feature of our algorithm is that it can be used for either non-overlapping or overlapping communities. #R##N#We test our algorithm using the LFR benchmark created by Lancichinetti, Fortunato, and Radicchi specifically for the purpose of evaluating such algorithms. Our algorithm can compete with the best of algorithms for both non-overlapping and overlapping communities as found in the comprehensive study of Lancichinetti and Fortunato.

Connected through hyperedge HE-1 (induced by source paper "semi supervised overlapping community finding based on label propagation with pairwise constraints"):
  with members:
    - "community structure detection in complex networks with partial background information"
    - "defining and evaluating network communities based on ground truth"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-2 (induced by source paper "arbitrary overlap constraints in graph packing problems"):
  with members:
    - "a classification for community discovery methods in complex networks"
    - "finding overlapping communities in social networks toward a rigorous approach"

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)