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: "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
  Abstract: Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is parallelized using MPI and OpenMP, and can exploit GPU accelerators by means of CUDA. We describe a performance comparison of DBCSR on systems with Intel Xeon Phi Knights Landing (KNL) processors, with respect to systems with Intel Xeon CPUs (including systems with GPUs). We find that the DBCSR on Cray XC40 KNL-based systems is 11%-14% slower than on a hybrid Cray XC50 with Nvidia P100 cards, at the same number of nodes. When compared to a Cray XC40 system equipped with dual-socket Intel Xeon CPUs, the KNL is up to 24% faster.

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"
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "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 "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"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "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:
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "sparse tensor algebra as a parallel programming model"

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)