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 "flashmatrix parallel scalable data analysis with generalized matrix operations using commodity ssds"):
  Abstract: FlashMatrix is a matrix-oriented programming framework for general data analysis with high-level functional programming interface. It scales matrix operations beyond memory capacity by utilizing solid-state drives (SSDs) in non-uniform memory architecture (NUMA). It provides a small number of generalized matrix operations (GenOps) and reimplements a large number of matrix operations in the R framework with GenOps. As such, it executes R code in parallel and out of core automatically. FlashMatrix uses vectorized user-defined functions (VUDF) to reduce the overhead of function calls and fuses matrix operations to reduce data movement between CPU and SSDs. We implement multiple machine learning algorithms in R to benchmark the performance of FlashMatrix. The execution of the R implementations in FlashMatrix has performance comparable to optimized C implementations. When scaling beyond memory capacity on a large parallel machine, the out-of-core execution of these R implementations in FlashMatrix has performance comparable to in-memory execution on a billion-scale dataset. Both in-memory and out-of-core execution significantly outperform the in-memory execution of Spark MLlib.

Includes members:
  Member "an ssd based eigensolver for spectral analysis on billion node graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi external memory sparse matrix multiplication for billion node graphs")
  Member "semi external memory sparse matrix multiplication on billion node graphs in a multicore architecture":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flashr r programmed parallel and scalable machine learning using ssds")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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)