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: "how hard is counting triangles in the streaming model"
  Abstract: The problem of (approximately) counting the number of triangles in a graph is one of the basic problems in graph theory. In this paper we study the problem in the streaming model. We study the amount of memory required by a randomized algorithm to solve this problem. In case the algorithm is allowed one pass over the stream, we present a best possible lower bound of $\Omega(m)$ for graphs $G$ with $m$ edges on $n$ vertices. If a constant number of passes is allowed, we show a lower bound of $\Omega(m/T)$, $T$ the number of triangles. We match, in some sense, this lower bound with a 2-pass $O(m/T^{1/3})$-memory algorithm that solves the problem of distinguishing graphs with no triangles from graphs with at least $T$ triangles. We present a new graph parameter $\rho(G)$ -- the triangle density, and conjecture that the space complexity of the triangles problem is $\Omega(m/\rho(G))$. We match this by a second algorithm that solves the distinguishing problem using $O(m/\rho(G))$-memory.

Connected through hyperedge HE-1 (induced by source paper "fleet butterfly estimation from a bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "edge sample and discard a new algorithm for counting triangles in large dynamic graphs"
    - "on sampling from massive graph streams"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"

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 "counting butterfies from a large bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "on sampling from massive graph streams"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "evolution of directed triangle motifs in the google osn"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triangle counting in dynamic graph streams"

Connected through hyperedge HE-4 (induced by source paper "fast and accurate graph stream summarization"):
  with members:
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "gsketch on query estimation in graph streams"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-5 (induced by source paper "streaming verification of graph properties"):
  with members:
    - "bipartite perfect matching is in quasi nc"
    - "rational proofs with multiple provers"
    - "tight bounds for linear sketches of approximate matchings"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "sketching cuts in graphs and hypergraphs"
    - "an improved interactive streaming algorithm for the distinct elements problem"
    - "semi streaming algorithms for annotated graph streams"

Connected through hyperedge HE-6 (induced by source paper "a hybrid sampling scheme for triangle counting"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "a second look at counting triangles in graph streams"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "improved graph sampling for triangle counting"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "information cost tradeoffs for augmented index and streaming language recognition"

Connected through hyperedge HE-8 (induced by source paper "on summarizing graph streams"):
  with members:
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "gsketch on query estimation in graph streams"

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)