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 fast learning algorithm for image segmentation with max pooling convolutional networks"
  Abstract: We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. #R##N#We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times.

Connected through hyperedge HE-1 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural network pruning to accelerate membrane segmentation in electron microscopy"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "an iterative convolutional neural network algorithm improves electron microscopy image segmentation"
    - "learning both weights and connections for efficient neural networks"
    - "structured pruning of deep convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "toward streaming synapse detection with compositional convnets"):
  with members:
    - "deep tensor convolution on multicores"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep learning convolutional networks for multiphoton microscopy vasculature segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "theano a python framework for fast computation of mathematical expressions"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "sparse convolutional networks using the permutohedral lattice"
    - "an iterative convolutional neural network algorithm improves electron microscopy image segmentation"

Connected through hyperedge HE-5 (induced by source paper "znni maximizing the inference throughput of 3d convolutional networks on multi core cpus and gpus"):
  with members:
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "multi scale context aggregation by dilated convolutions"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"
    - "fully convolutional networks for semantic segmentation"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-6 (induced by source paper "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "the potential of the intel xeon phi for supervised deep learning"
    - "locally scale invariant convolutional neural networks"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"
    - "fully convolutional networks for semantic segmentation"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-7 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

Connected through hyperedge HE-8 (induced by source paper "recursive training of 2d 3d convolutional networks for neuronal boundary detection"):
  with members:
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "fully convolutional networks for semantic segmentation"
    - "fast training of convolutional networks through ffts"

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)