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: "blasx a high performance level 3 blas library for heterogeneous multi gpu computing"
  Abstract: Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing. The massive and economic computing power brought forth by the emerging GPU architectures drives interest in implementation of compute-intensive level 3 BLAS on multi-GPU systems. In this paper, we investigate existing multi-GPU level 3 BLAS and present that 1) issues, such as the improper load balancing, inefficient communication, insufficient GPU stream level concurrency and data caching, impede current implementations from fully harnessing heterogeneous computing resources; 2) and the inter-GPU Peer-to-Peer(P2P) communication remains unexplored. We then present BLASX: a highly optimized multi-GPU level-3 BLAS. We adopt the concepts of algorithms-by-tiles treating a matrix tile as the basic data unit and operations on tiles as the basic task. Tasks are guided with a dynamic asynchronous runtime, which is cache and locality aware. The communication cost under BLASX becomes trivial as it perfectly overlaps communication and computation across multiple streams during asynchronous task progression. It also takes the current tile cache scheme one step further by proposing an innovative 2-level hierarchical tile cache, taking advantage of inter-GPU P2P communication. As a result, linear speedup is observable with BLASX under multi-GPU configurations; and the extensive benchmarks demonstrate that BLASX consistently outperforms the related leading industrial and academic projects such as cuBLAS-XT, SuperMatrix, MAGMA and PaRSEC.

Connected through hyperedge HE-1 (induced by source paper "ism2 optimizing irregular shaped matrix matrix multiplication on gpus"):
  with members:
    - "improving performance of iterative methods by lossy checkponting"
    - "kblas an optimized library for dense matrix vector multiplication on gpu accelerators"

Connected through hyperedge HE-2 (induced by source paper "superneurons dynamic gpu memory management for training deep neural networks"):
  with members:
    - "memory efficient implementation of densenets"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "efficient communications in training large scale neural networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "eie efficient inference engine on compressed deep neural network"
    - "training deep nets with sublinear memory cost"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "superneurons fft based gradient sparsification in the distributed training of deep neural networks"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "learning compact recurrent neural networks with block term tensor decomposition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-4 (induced by source paper "learning compact recurrent neural networks with block term tensor decomposition"):
  with members:
    - "tensor regression networks"
    - "tensor train recurrent neural networks for video classification"
    - "compressing recurrent neural network with tensor train"
    - "tensorial recurrent neural networks for longitudinal data analysis"
    - "sharing residual units through collective tensor factorization in deep neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "learning efficient tensor representations with ring structure networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "accelerating the computation of flapw methods on heterogeneous architectures"):
  with members:
    - "billion scale similarity search with gpus"
    - "high performance generation of the hamiltonian and overlap matrices in flapw methods"
    - "hybrid cpu gpu generation of the hamiltonian and overlap matrices in flapw methods"
    - "computing petaflops over terabytes of data the case of genome wide association studies"

Connected through hyperedge HE-6 (induced by source paper "accelerating deep neural network training with inconsistent stochastic gradient descent"):
  with members:
    - "efficient communications in training large scale neural networks"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "caffe convolutional architecture for fast feature embedding"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "efficient communications in training large scale neural networks"):
  with members:
    - "accelerating deep neural network training with inconsistent stochastic gradient descent"
    - "caffe convolutional architecture for fast feature embedding"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "a reliable effective terascale linear learning system"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-8 (induced by source paper "accelerating blas on custom architecture through algorithm architecture co design"):
  with members:
    - "accelerating blas and lapack via efficient floating point architecture design"

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)