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 residual networks with exponential linear unit"
  Abstract: The depth of convolutional neural networks is a crucial ingredient for reduction in test errors on benchmarks like ImageNet and COCO. However, training a neural network becomes difficult with increasing depth. Problems like vanishing gradient and diminishing feature reuse are quite trivial in very deep convolutional neural networks.   The notable recent contributions towards solving these problems and simplifying the training of very deep models are Residual and Highway Networks. These networks allow earlier representations (from the input or those learned in earlier layers) to flow unimpededly to later layers through skip connections. Such very deep models with hundreds or more layers have lead to a considerable decrease in test errors, on benchmarks like ImageNet and COCO.   In this paper, we propose to replace the combination of ReLU and Batch Normalization with Exponential Linear Unit (ELU) in Residual Networks. Our experiments show that this not only speeds up the learning behavior in Residual Networks, but also improves the classification performance as the depth increases. Our model increases the accuracy on datasets like CIFAR-10 and CIFAR-100 by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "spherical view synthesis for self supervised 360 depth estimation"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "single view stereo matching"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "learning structure from motion from motion"

Connected through hyperedge HE-3 (induced by source paper "disco depth inference from stereo using context"):
  with members:
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-4 (induced by source paper "residual networks of residual networks multilevel residual networks"):
  with members:
    - "weighted residuals for very deep networks"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "convolutional residual memory networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "swapout learning an ensemble of deep architectures"

Connected through hyperedge HE-5 (induced by source paper "electron proton dynamics in deep learning"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "sgd learns the conjugate kernel class of the network"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "how to escape saddle points efficiently"
    - "convergence analysis of two layer neural networks with relu activation"
    - "reliably learning the relu in polynomial time"

Connected through hyperedge HE-6 (induced by source paper "pyramidal ror for image classification"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "residual attention network for image classification"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "deep pyramidal residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "flexible rectified linear units for improving convolutional neural networks"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "self normalizing neural networks"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "revise saturated activation functions"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "improving deep neural network with multiple parametric exponential linear units"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "all you need is a good init"

Connected through hyperedge HE-8 (induced by source paper "individual recognition in schizophrenia using deep learning methods with random forest and voting classifiers insights from resting state eeg streams"):
  with members:
    - "matrix neural networks"
    - "deep learning in neural networks"
    - "deep learning using linear support vector machines"

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)