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 "a high performance implementation of spectral clustering on cpu gpu platforms"):
  Abstract: Spectral clustering is one of the most popular graph clustering algorithms, which achieves the best performance for many scientific and engineering applications. However, existing implementations in commonly used software platforms such as Matlab and Python do not scale well for many of the emerging Big Data applications. In this paper, we present a fast implementation of the spectral clustering algorithm on a CPU-GPU heterogeneous platform. Our implementation takes advantage of the computational power of the multi-core CPU and the massive multithreading and SIMD capabilities of GPUs. Given the input as data points in high dimensional space, we propose a parallel scheme to build a sparse similarity graph represented in a standard sparse representation format. Then we compute the smallest k eigenvectors of the Laplacian matrix by utilizing the reverse communication interfaces of ARPACK software and cuSPARSE library, where k is typically very large. Moreover, we implement a very fast parallelized k-means algorithm on GPUs. Our implementation is shown to be significantly faster compared to the best known Matlab and Python implementations for each step. In addition, our algorithm scales to problems with a very large number of clusters.

Includes members:
  Member "a data driven approach to extract connectivity structures from diffusion tensor imaging data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network summarization with preserved spectral properties")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")

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.DC(Distributed, Parallel, and Cluster Computing)