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 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.

Connected through hyperedge HE-1 (induced by source paper "bandwidth optimized parallel algorithms for sparse matrix matrix multiplication using propagation blocking"):
  with members:
    - "fast randomized pca for sparse data"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "network summarization with preserved spectral properties"):
  with members:
    - "network density of states"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "solving graph compression via optimal transport"
    - "netlsd hearing the shape of a graph"
    - "how powerful are graph neural networks"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"

Connected through hyperedge HE-3 (induced by source paper "streaming graph challenge stochastic block partition"):
  with members:
    - "static graph challenge subgraph isomorphism"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "an incremental local first community detection method for dynamic graphs"
    - "fast detection of overlapping communities via online tensor methods"
    - "bayesian discovery of threat networks"
    - "network sampling from static to streaming graphs"
    - "efficient and principled method for detecting communities in networks"

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.DC(Distributed, Parallel, and Cluster Computing)