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: "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
  Abstract: Generalized sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. Here we show that SpGEMM also yields efficient algorithms for general sparse-matrix indexing in distributed memory, provided that the underlying SpGEMM implementation is sufficiently flexible and scalable. We demonstrate that our parallel SpGEMM methods, which use two-dimensional block data distributions with serial hypersparse kernels, are indeed highly flexible, scalable, and memory-efficient in the general case. This algorithm is the first to yield increasing speedup on an unbounded number of processors; our experiments show scaling up to thousands of processors in a variety of test scenarios.

Connected through hyperedge HE-1 (induced by source paper "the parallelism motifs of genomic data analysis"):
  with members:
    - "dibella distributed long read to long read alignment"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "extreme scale de novo metagenome assembly"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "optimizing high performance markov clustering for pre exascale architectures"):
  with members:
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-3 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "shared memory parallelization of mttkrp for dense tensors"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "hptt a high performance tensor transposition c library"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "smash co designing software compression and hardware accelerated indexing for efficient sparse matrix operations"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "sparse matrix multiplication on cam based accelerator"
    - "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"
    - "graphr accelerating graph processing using reram"
    - "snap a general purpose network analysis and graph mining library"
    - "sparse 3d convolutional neural networks"
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"

Connected through hyperedge HE-5 (induced by source paper "communication efficient jaccard similarity for high performance distributed genome comparisons"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "a note on the triangle inequality for the jaccard distance"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "sparse tensor algebra as a parallel programming model"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-6 (induced by source paper "parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model"):
  with members:
    - "ductteip an efficient programming model for distributed task based parallel computing"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "chunks and tasks a programming model for parallelization of dynamic algorithms"

Connected through hyperedge HE-7 (induced by source paper "topological computing of arrangements with co chains"):
  with members:
    - "geometric computing with chain complexes design and features of a julia package"
    - "julia a fresh approach to numerical computing"
    - "chain based representations for solid and physical modeling"

Connected through hyperedge HE-8 (induced by source paper "parallel memory efficient all at once algorithms for the sparse matrix triple products in multigrid methods"):
  with members:
    - "a general purpose hierarchical mesh partitioning method with node balancing strategies for large scale numerical simulations"
    - "hypergraph partitioning for sparse matrix matrix multiplication"

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)