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: "deep tensor convolution on multicores"
  Abstract: Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features. These networks have improved performance of video and volumetric image analysis, but have been limited in size due to the low memory ceiling of GPU hardware. Existing CPU implementations overcome this constraint but are impractically slow. Here we extend and optimize the faster Winograd-class of convolutional algorithms to the $N$-dimensional case and specifically for CPU hardware. First, we remove the need to manually hand-craft algorithms by exploiting the relaxed constraints and cheap sparse access of CPU memory. Second, we maximize CPU utilization and multicore scalability by transforming data matrices to be cache-aware, integer multiples of AVX vector widths. Treating 2-dimensional ConvNets as a special (and the least beneficial) case of our approach, we demonstrate a 5 to 25-fold improvement in throughput compared to previous state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "dwm a decomposable winograd method for convolution acceleration"):
  with members:
    - "winograd convolution for dnns beyond linear polynomials"
    - "efficient winograd convolution via integer arithmetic"
    - "efficient winograd or cook toom convolution kernel implementation on widely used mobile cpus"
    - "compression of deep convolutional neural networks under joint sparsity constraints"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "efficient sparse winograd convolutional neural networks"
    - "enabling sparse winograd convolution by native pruning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "l3 fusion fast transformed convolutions on cpus"):
  with members:
    - "efficient sparse winograd convolutional neural networks"
    - "deep residual learning for image recognition"
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

Connected through hyperedge HE-3 (induced by source paper "optimizing cnn model inference on cpus"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "learning to optimize tensor programs"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "scheduling computation graphs of deep learning models on manycore cpus"

Connected through hyperedge HE-4 (induced by source paper "fft convolutions are faster than winograd on modern cpus here is why"):
  with members:
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-5 (induced by source paper "morphological error detection in 3d segmentations"):
  with members:
    - "toward streaming synapse detection with compositional convnets"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "flood filling networks"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "multiscale hierarchical convolutional networks"):
  with members:
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "densely connected convolutional networks"
    - "learning compact recurrent neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

Connected through hyperedge HE-7 (induced by source paper "toward streaming synapse detection with compositional convnets"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "a context aware delayed agglomeration framework for electron microscopy segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"

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.CV(Computer Vision and Pattern Recognition)