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 "in a world that counts clustering and detecting fake social engagement at scale"):
  Abstract: How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. With the domain knowledge of spammer seeds, we formulate and tackle the problem in a semi-supervised manner --- with the objective of searching for individuals that have similar pattern of behavior as the known seeds --- based on a graph diffusion process via local spectral subspace. We offer a fast, scalable MapReduce deployment adapted from the localized spectral clustering algorithm. We demonstrate the effectiveness of our deployment at Google by achieving a manual review accuracy of 98% on YouTube Comments graph in practice. Comparing with the state-of-the-art algorithm CopyCatch, Leas achieves 10 times faster running time on average. Leas is now actively in use at Google, searching for daily deceptive practices on YouTube's engagement graph spanning over a billion users.

Includes members:
  Member "overlapping community detection via local spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable and robust local community detection via adaptive subgraph extraction and diffusions")
  Member "uncovering the small community structure in large networks a local spectral approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "paying for likes understanding facebook like fraud using honeypots":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the art of social bots a review and a refined taxonomy")
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning with relational information for short sequences")
  Member "semi supervised eigenvectors for large scale locally biased learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an optimization approach to locally biased graph algorithms")
  Member "network analysis of recurring youtube spam campaigns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language processing sentiment analysis and clinical analytics")
  Member "finding endogenously formed communities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "network essence pagerank completion and centrality conforming markov chains")

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)