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 "counting triangles in real world graph streams dealing with repeated edges and time windows"):
  Abstract: Real-world graphs often manifest as a massive temporal stream of edges. The need for real-time analysis of such large graph streams has led to progress on low memory, one-pass streaming graph algorithms. These algorithms were designed for simple graphs, assuming an edge is not repeated in the stream. Real graph streams however, are almost always multigraphs i.e., they contain many duplicate edges. The assumption of no repeated edges requires an extra pass *storing all the edges* just for deduplication, which defeats the purpose of small memory algorithms. #R##N#We describe an algorithm for estimating the triangle count of a multigraph stream of edges. We show that all previous streaming algorithms for triangle counting fail for multigraph streams, despite their impressive accuracies for simple graphs. The bias created by duplicate edges is a major problem, and leads these algorithms astray. Our algorithm avoids these biases through careful debiasing strategies and has provable theoretical guarantees and excellent empirical performance. Our algorithm builds on the previously introduced wedge sampling methodology. #R##N#Another challenge in analyzing temporal graphs is finding the right temporal window size. Our algorithm seamlessly handles multiple time windows, and does not require committing to any window size(s) a priori. We apply our algorithm to discover fascinating transitivity and triangle trends in real-world graph streams.

Includes members:
  Member "graph sample and hold a framework for big graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph similarity learning a survey")
  Member "wedge sampling for computing clustering coefficients and triangle counts on large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the power of pivoting for exact clique counting")
  Member "triadic measures on graphs the power of wedge sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "parallel triangle counting in massive streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fleet butterfly estimation from a bipartite graph stream")
  Member "a space efficient streaming algorithm for triangle counting using the birthday paradox":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding the limitations of network online learning")
  Member "degree relations of triangles in real world networks and graph models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "retrieving top weighted triangles in graphs")
  Member "community structure and scale free collections of erdős renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "artificial benchmark for community detection abcd fast random graph model with community structure")

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.DS(Data Structures and Algorithms)