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 "graph summarization methods and applications a survey"):
  Abstract: While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

Includes members:
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "by chance is not enough preserving relative density through non uniform sampling":
    also connected through other hyperedges:
      (none)
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "compressing graphs and indexes with recursive graph bisection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "techniques for inverted index compression")
  Member "network structure inference a survey motivations methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling interpretable generative parameters of random and real world graphs")
  Member "efficient frequent directions algorithm for sparse matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "pnp fast path ensemble method for movie design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "career transitions and trajectories a case study in computing")
  Member "an empirical comparison of the summarization power of graph clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph partitioning based diffusion convolution recurrent neural network for large scale traffic forecasting")

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.IR(Information Retrieval)