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: "an ssd based eigensolver for spectral analysis on billion node graphs"
  Abstract: Many eigensolvers such as ARPACK and Anasazi have been developed to compute eigenvalues of a large sparse matrix. These eigensolvers are limited by the capacity of RAM. They run in memory of a single machine for smaller eigenvalue problems and require the distributed memory for larger problems. #R##N#In contrast, we develop an SSD-based eigensolver framework called FlashEigen, which extends Anasazi eigensolvers to SSDs, to compute eigenvalues of a graph with hundreds of millions or even billions of vertices in a single machine. FlashEigen performs sparse matrix multiplication in a semi-external memory fashion, i.e., we keep the sparse matrix on SSDs and the dense matrix in memory. We store the entire vector subspace on SSDs and reduce I/O to improve performance through caching the most recent dense matrix. Our result shows that FlashEigen is able to achieve 40%-60% performance of its in-memory implementation and has performance comparable to the Anasazi eigensolvers on a machine with 48 CPU cores. Furthermore, it is capable of scaling to a graph with 3.4 billion vertices and 129 billion edges. It takes about four hours to compute eight eigenvalues of the billion-node graph using 120 GB memory.

Connected through hyperedge HE-1 (induced by source paper "semi external memory sparse matrix multiplication for billion node graphs"):
  with members:
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "standards for graph algorithm primitives"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "flashr r programmed parallel and scalable machine learning using ssds"):
  with members:
    - "semi external memory sparse matrix multiplication on billion node graphs in a multicore architecture"
    - "mllib machine learning in apache spark"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "semi external memory sparse matrix multiplication on billion node graphs in a multicore architecture"):
  with members:
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "standards for graph algorithm primitives"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "flashmatrix parallel scalable data analysis with generalized matrix operations using commodity ssds"):
  with members:
    - "semi external memory sparse matrix multiplication on billion node graphs in a multicore architecture"
    - "defining and evaluating network communities based on ground truth"

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)