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: "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
  Abstract: This paper describes maxDNN, a computationally efficient convolution kernel for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3% computational efficiency on typical deep learning network architectures. The design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We only address forward propagation (FPROP) operation of the network, but we believe that the same techniques used here will be effective for backward propagation (BPROP) as well.

Connected through hyperedge HE-1 (induced by source paper "pytorch an imperative style high performance deep learning library"):
  with members:
    - "forward modeling for partial observation strategy games a starcraft defogger"
    - "starcraft ii a new challenge for reinforcement learning"
    - "automatic differentiation in machine learning a survey"
    - "fast algorithms for convolutional neural networks"
    - "julia a fresh approach to numerical computing"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-2 (induced by source paper "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a comparison of cnn based face and head detectors for real time video surveillance applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "dissecting the nvidia volta gpu architecture via microbenchmarking"):
  with members:
    - "dissecting gpu memory hierarchy through microbenchmarking"

Connected through hyperedge HE-4 (induced by source paper "cbinfer change based inference for convolutional neural networks on video data"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "origami a 803 gop s w convolutional network accelerator"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"
    - "hardware oriented approximation of convolutional neural networks"
    - "clockwork convnets for video semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "origami a 803 gop s w convolutional network accelerator"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "flownet learning optical flow with convolutional networks"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "compression of deep neural networks on the fly"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "development of javascript based deep learning platform and application to distributed training"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "deep residual learning for image recognition"
    - "8 bit approximations for parallelism in deep learning"
    - "implementation of a practical distributed calculation system with browsers and javascript and application to distributed deep learning"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "mlitb machine learning in the browser"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "theano mpi a theano based distributed training framework"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-8 (induced by source paper "pruning convolutional neural networks for resource efficient inference"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "learning the number of neurons in deep networks"
    - "learning both weights and connections for efficient neural networks"
    - "data free parameter pruning for deep neural networks"
    - "fast convnets using group wise brain damage"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

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.NE(Neural and Evolutionary Computing)