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: "high performance neural networks for visual object classification"
  Abstract: We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.

Connected through hyperedge HE-1 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-2 (induced by source paper "a multigrid method for efficiently training video models"):
  with members:
    - "stm spatiotemporal and motion encoding for action recognition"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "long term feature banks for detailed video understanding"
    - "evolving space time neural architectures for videos"
    - "video action transformer network"
    - "neural rejuvenation improving deep network training by enhancing computational resource utilization"

Connected through hyperedge HE-3 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "randomly weighted cnns for music audio classification"
    - "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-4 (induced by source paper "an algorithm for routing capsules in all domains"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "visualizing and measuring the geometry of bert"
    - "on the variance of the adaptive learning rate and beyond"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "semi supervised and task driven data augmentation"):
  with members:
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "pathology segmentation using distributional differences to images of healthy origin"
    - "data augmentation for skin lesion analysis"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "learning to segment medical images with scribble supervision alone"
    - "mixup beyond empirical risk minimization"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "chaos a parallelization scheme for training convolutional neural networks on intel xeon phi"):
  with members:
    - "comparative study of deep learning software frameworks"
    - "benchmarking state of the art deep learning software tools"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "one weird trick for parallelizing convolutional neural networks"
    - "multi gpu training of convnets"
    - "comparative performance analysis of intel xeon phi gpu and cpu"

Connected through hyperedge HE-7 (induced by source paper "exponential discriminative metric embedding in deep learning"):
  with members:
    - "binary generative adversarial networks for image retrieval"
    - "learning in high dimensional multimedia data the state of the art"
    - "funnel structured cascade for multi view face detection with alignment awareness"
    - "a survey on learning to hash"
    - "wide residual networks"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"

Connected through hyperedge HE-8 (induced by source paper "rice classification using spatio spectral deep convolutional neural network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "expectation propagation a probabilistic view of deep feed forward networks"
    - "searching for activation functions"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "understanding neural networks through deep visualization"
    - "u net convolutional networks for biomedical image 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.AI(Artificial Intelligence)