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: "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
  Abstract: Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix-matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "high performance sparse matrix matrix products on intel knl and multicore architectures"
    - "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
    - "a survey of sparse matrix vector multiplication performance on large matrices"

Connected through hyperedge HE-2 (induced by source paper "sparse matrix multiplication with bandwidth restricted all to all communication"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "towards a complexity theory for the congested clique"
    - "tight bounds for distributed graph computations"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "derandomizing local distributed algorithms under bandwidth restrictions"

Connected through hyperedge HE-3 (induced by source paper "sparse matrix multiplication and triangle listing in the congested clique model"):
  with members:
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "triangle finding and listing in congest networks"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "on the distributed complexity of large scale graph computations"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"):
  with members:
    - "semi external memory sparse matrix multiplication for billion node graphs"
    - "early evaluation of intel optane non volatile memory with hpc i o workloads"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "standards for graph algorithm primitives"

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)