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: "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.

Connected through hyperedge HE-1 (induced by source paper "counting triangles in massive graphs with mapreduce"):
  with members:
    - "triadic measures on graphs the power of wedge sampling"
    - "parallel triangle counting in massive streaming graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"
    - "degree relations of triangles in real world networks and graph models"
    - "community structure and scale free collections of erdős renyi graphs"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "the anatomy of the facebook social graph"

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.DS(Data Structures and Algorithms)