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 "efficiently detecting overlapping communities using seeding and semi supervised learning"):
  Abstract: A common scheme for discovering overlapping communities in a network is to use a seeding process followed by an expansion process. Most seeding methods are either too complex to scale to large networks or too simple to select high-quality seeds. Additionally, the non-principled functions used by most expansion methods lead to poor performances when applied to diverse networks. This paper proposes a new method that transforms a network into a corpus. Each edge is treated as a document, and all the network nodes are treated as terms of the corpus. We propose an effective seeding method that selects seeds as a training set, and a principled expansion method based on semi-supervised learning that classifies the edges. We compared our new algorithm with four other community detection algorithms on a wide range of synthetic and empirical networks. Our experimental results show that the new algorithm significantly improved the clustering performance in most cases. Furthermore, the time complexity of the new algorithm is linear with respect to the number of edges, which means that the technique can be scaled to large networks.

Includes members:
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences")
  Member "social structure of facebook networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks")
  Member "efficient and principled method for detecting communities in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "detect overlapping and hierarchical community structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ensemble based overlapping community detection using disjoint community structures")

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)