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: "imagenet pre trained models with batch normalization"
  Abstract: Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with popular state-of-the-art architectures for the Caffe framework. The first release includes Residual Networks (ResNets) with generation script as well as the batch-normalization-variants of AlexNet and VGG19. All models outperform previous models with the same architecture. The models and training code are available at this http URL and this https URL

Connected through hyperedge HE-1 (induced by source paper "learning embeddings into entropic wasserstein spaces"):
  with members:
    - "stochastic wasserstein barycenters"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "deep contextualized word representations"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "learning with a wasserstein loss"
    - "order embeddings of images and language"

Connected through hyperedge HE-2 (induced by source paper "improving the convergence of sgd through adaptive batch sizes"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "understanding deep learning requires rethinking generalization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "coupling adaptive batch sizes with learning rates"

Connected through hyperedge HE-3 (induced by source paper "self supervised data bootstrapping for deep optical character recognition of identity documents"):
  with members:
    - "multi oriented text detection with fully convolutional networks"
    - "synthetic data for text localisation in natural images"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "reading scene text in deep convolutional sequences"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-4 (induced by source paper "modanet a large scale street fashion dataset with polygon annotations"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "deformable convolutional networks"
    - "annotating object instances with a polygon rnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep learning markov random field for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "quenn quantization engine for low power neural networks"):
  with members:
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-6 (induced by source paper "a systematic study of the class imbalance problem in convolutional neural networks"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "the inaturalist species classification and detection dataset"
    - "detection of concealed cars in complex cargo x ray imagery using deep learning"
    - "relay backpropagation for effective learning of deep convolutional neural networks"
    - "recent advances in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "robust place categorization with deep domain generalization"):
  with members:
    - "autodial automatic domain alignment layers"
    - "learning deep nbnn representations for robust place categorization"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "deep learning features at scale for visual place recognition"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "revisiting batch normalization for practical domain adaptation"
    - "2d visual place recognition for domestic service robots at night"

Connected through hyperedge HE-8 (induced by source paper "exploring correlations in multiple facial attributes through graph attention network"):
  with members:
    - "pseudo task augmentation from deep multitask learning to intratask sharing and back"
    - "augmentor an image augmentation library for machine learning"
    - "an overview of multi task learning in deep neural networks"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "cross stitch networks for multi task learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "semi supervised classification with graph convolutional networks"
    - "trace norm regularised deep multi task learning"

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)