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: "tensor contractions with extended blas kernels on cpu and gpu"
  Abstract: Tensor contractions constitute a key computational ingredient of numerical multi-linear algebra. However, as the order and dimension of tensors grow, the time and space complexities of tensor-based computations grow quickly. Existing approaches for tensor contractions typically involves explicit copy and transpose operations. In this paper, we propose and evaluate a new BLAS-like primitive STRIDEDBATCHEDGEMM that is capable of performing a wide range of tensor contractions on CPU and GPU efficiently. Through systematic benchmarking, we demonstrate the advantages of our approach over conventional approaches. Concretely, we implement the Tucker decomposition and show that using our kernels yields 100x speedup as compared to the implementation using existing state-of-the-art libraries.

Connected through hyperedge HE-1 (induced by source paper "high performance deep learning via a single building block"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "large batch training for lstm and beyond"
    - "nvidia tensor core programmability performance precision"
    - "high accuracy low precision training"
    - "glow graph lowering compiler techniques for neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications"):
  with members:
    - "design of a high performance gemm like tensor tensor multiplication"
    - "high performance tensor contraction without transposition"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "performance modeling for dense linear algebra"

Connected through hyperedge HE-3 (induced by source paper "high performance zero memory overhead direct convolutions"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "deep residual learning for image recognition"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "caffe convolutional architecture for fast feature embedding"
    - "compressing deep convolutional networks using vector quantization"

Connected through hyperedge HE-4 (induced by source paper "tensor regression networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "tensor contraction layers for parsimonious deep nets"
    - "deep multi task representation learning a tensor factorisation approach"
    - "training input output recurrent neural networks through spectral methods"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "tensorizing neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "tensor contraction layers for parsimonious deep nets"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "training input output recurrent neural networks through spectral methods"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "global optimality in tensor factorization deep learning and beyond"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "acceleration of tensor product operations for high order finite element methods"):
  with members:
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "gpu accelerated spectral finite elements on all hex meshes"

Connected through hyperedge HE-7 (induced by source paper "design of a high performance gemm like tensor tensor multiplication"):
  with members:
    - "hptt a high performance tensor transposition c library"
    - "ttc a high performance compiler for tensor transpositions"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "ttc a tensor transposition compiler for multiple architectures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "cudnn efficient primitives for deep learning"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-8 (induced by source paper "tensorly tensor learning in python"):
  with members:
    - "tensor regression networks"
    - "tensorizing generative adversarial nets"
    - "tensorizing neural networks"
    - "tensor vs matrix methods robust tensor decomposition under block sparse perturbations"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "on the expressive power of deep learning a tensor analysis"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "api design for machine learning software experiences from the scikit learn project"

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)