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: "a second look at counting triangles in graph streams"
  Abstract: In this paper we present improved results on the problem of counting triangles in edge streamed graphs. For graphs with $m$ edges and at least $T$ triangles, we show that an extra look over the stream yields a two-pass streaming algorithm that uses $O(\frac{m}{\epsilon^{4.5}\sqrt{T}})$ space and outputs a $(1+\epsilon)$ approximation of the number of triangles in the graph. This improves upon the two-pass streaming tester of Braverman, Ostrovsky and Vilenchik, ICALP 2013, which distinguishes between triangle-free graphs and graphs with at least $T$ triangle using $O(\frac{m}{T^{1/3}})$ space. Also, in terms of dependence on $T$, we show that more passes would not lead to a better space bound. In other words, we prove there is no constant pass streaming algorithm that distinguishes between triangle-free graphs from graphs with at least $T$ triangles using $O(\frac{m}{T^{1/2+\rho}})$ space for any constant $\rho \ge 0$.

Connected through hyperedge HE-1 (induced by source paper "temporal network sampling"):
  with members:
    - "from community to role based graph embeddings"
    - "temporal network representation learning"
    - "dynamic joint variational graph autoencoders"
    - "influential user subscription on time decaying social streams"
    - "explanations for temporal recommendations"
    - "sampling for approximate bipartite network projection"
    - "on sampling from massive graph streams"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "the sketching complexity of graph and hypergraph counting"):
  with members:
    - "size bounds and query plans for relational joins"
    - "estimating graph parameters from random order streams"
    - "on estimating maximum matching size in graph streams"
    - "on the multiparty communication complexity of testing triangle freeness"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "on approximating functions of the singular values in a stream"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"

Connected through hyperedge HE-3 (induced by source paper "a hybrid sampling scheme for triangle counting"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "how hard is counting triangles in the streaming model"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "colorful triangle counting and a mapreduce implementation"
    - "information cost tradeoffs for augmented index and streaming language recognition"

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)