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: "approximate triangle counting"
  Abstract: Triangle counting is an important problem in graph mining. Clustering coefficients of vertices and the transitivity ratio of the graph are two metrics often used in complex network analysis. Furthermore, triangles have been used successfully in several real-world applications. However, exact triangle counting is an expensive computation. In this paper we present the analysis of a practical sampling algorithm for counting triangles in graphs. Our analysis yields optimal values for the sampling rate, thus resulting in tremendous speedups ranging from \emph{2800}x to \emph{70000}x when applied to real-world networks. At the same time the accuracy of the estimation is excellent. #R##N#Our contributions include experimentation on graphs with several millions of nodes and edges, where we show how practical our proposed method is. Finally, our algorithm's implementation is a part of the \pegasus library (Code and datasets are available at (this http URL).) a Peta-Graph Mining library implemented in Hadoop, the open source version of Mapreduce.

Connected through hyperedge HE-1 (induced by source paper "distributed balanced partitioning via linear embedding"):
  with members:
    - "spinner scalable graph partitioning in the cloud"
    - "finding connected components on map reduce in logarithmic rounds"
    - "defining and evaluating network communities based on ground truth"
    - "scalable k means"
    - "fast clustering using mapreduce"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-2 (induced by source paper "reinventing the triangles rule of thumb for assessing detectability"):
  with members:
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectra of random graphs with arbitrary expected degrees"
    - "graph spectra and the detectability of community structure in networks"

Connected through hyperedge HE-3 (induced by source paper "efficient triangle counting in large graphs via degree based vertex partitioning"):
  with members:
    - "colorful triangle counting and a mapreduce implementation"
    - "mach fast randomized tensor decompositions"

Connected through hyperedge HE-4 (induced by source paper "mach fast randomized tensor decompositions"):
  with members:
    - "are tensor decomposition solutions unique on the global convergence of hosvd and parafac algorithms"

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)