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: "a bandwidth saving optimization for mpi broadcast collective operation"
  Abstract: The efficiency and scalability of MPI collective operations, in particular the broadcast operation, plays an integral part in high performance computing applications. MPICH, as one of the contemporary widely-used MPI software stacks, implements the broadcast operation based on point-to-point operation. Depending on the parameters, such as message size and process count, the library chooses to use different algorithms, as for instance binomial dissemination, recursive-doubling exchange or ring all-to-all broadcast (all-gather). However, the existing broadcast design in latest release of MPICH does not provide good performance for large messages (lmsg) or medium messages with non-power-of-two process counts (mmsg-npof2) due to the inner suboptimal ring all gather algorithm. In this paper, based on the native broadcast design in MPICH, we propose a tuned broadcast approach with bandwidth-saving in mind catering to the case of lmsg and mmsg-npof2. Several comparisons of the native and tuned broadcast designs are made for different data sizes and program sizes on Cray XC40 cluster. The results show that the performance of the tuned broadcast design can get improved by a range from 2% to 54% for lmsg and mmsg-npof2 in terms of user-level testing.

Connected through hyperedge HE-1 (induced by source paper "optimized broadcast for deep learning workloads on dense gpu infiniband clusters mpi or nccl"):
  with members:
    - "neural machine translation and sequence to sequence models a tutorial"
    - "edinburgh neural machine translation systems for wmt 16"
    - "deep learning in neural networks"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "towards performance portability through locality awareness for applications using one sided communication primitives"):
  with members:
    - "leveraging mpi 3 shared memory extensions for efficient pgas runtime systems"
    - "dart mpi an mpi based implementation of a pgas runtime system"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"
    - "design and implementation of mpich2 over infiniband with rdma support"

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)