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: "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
  Abstract: Matrix-matrix multiplication is a basic operation in linear algebra and an essential building block for a wide range of algorithms in various scientific fields. Theory and implementation for the dense, square matrix case are well-developed. If matrices are sparse, with application-specific sparsity patterns, the optimal implementation remains an open question. Here, we explore the performance of communication reducing 2.5D algorithms and one-sided MPI communication in the context of linear scaling electronic structure theory. In particular, we extend the DBCSR sparse matrix library, which is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. The library is specifically designed to efficiently perform block-sparse matrix-matrix multiplication of matrices with a relatively large occupation. Here, we compare the performance of the original implementation based on Cannon's algorithm and MPI point-to-point communication, with an implementation based on MPI one-sided communications (RMA), in both a 2D and a 2.5D approach. The 2.5D approach trades memory and auxiliary operations for reduced communication, which can lead to a speedup if communication is dominant. The 2.5D algorithm is somewhat easier to implement with one-sided communications. A detailed description of the implementation is provided, also for non ideal processor topologies, since this is important for actual applications. Given the importance of the precise sparsity pattern, and even the actual matrix data, which decides the effective fill-in upon multiplication, the tests are performed within the CP2K package with application benchmarks. Results show a substantial boost in performance for the RMA based 2.5D algorithm, up to 1.80x, which is observed to increase with the number of processes involved in the parallelization.

Connected through hyperedge HE-1 (induced by source paper "red blue pebbling revisited near optimal parallel matrix matrix multiplication"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "pushing the bounds for matrix matrix multiplication"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"
    - "i o efficient algorithms for matrix computations"

Connected through hyperedge HE-2 (induced by source paper "dbcsr a library for dense matrix multiplications on distributed gpu accelerated systems"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "dbcsr a blocked sparse tensor algebra library"):
  with members:
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "sparse tensor algebra as a parallel programming model"

Connected through hyperedge HE-4 (induced by source paper "sparse matrix multiplication with bandwidth restricted all to all communication"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "mst in o 1 rounds of the congested clique"
    - "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-5 (induced by source paper "sparse matrix multiplication and triangle listing in the congested clique model"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "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"

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)