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: "multi digit number recognition from street view imagery using deep convolutional neural networks"
  Abstract: Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.

Connected through hyperedge HE-1 (induced by source paper "curriculum by texture"):
  with members:
    - "do better imagenet models transfer better"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "simple baselines for human pose estimation and tracking"
    - "automated curriculum learning for neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "cycada cycle consistent adversarial domain adaptation"
    - "teacher student curriculum learning"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"

Connected through hyperedge HE-3 (induced by source paper "adversarial incremental learning"):
  with members:
    - "large scale incremental learning"
    - "attending to discriminative certainty for domain adaptation"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-4 (induced by source paper "self supervised generalisation with meta auxiliary learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "cinic 10 is not imagenet or cifar 10"
    - "auxiliary tasks in multi task learning"
    - "end to end multi task learning with attention"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-5 (induced by source paper "on minimum discrepancy estimation for deep domain adaptation"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "a deep four stream siamese convolutional neural network with joint verification and identification loss for person re detection"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial feature augmentation for unsupervised domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "conditionally learn to pay attention for sequential visual task"):
  with members:
    - "learn to pay attention"
    - "a unified query based generative model for question generation and question answering"
    - "diversity driven attention model for query based abstractive summarization"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "hierarchical attention networks"
    - "progressive attention networks for visual attribute prediction"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-7 (induced by source paper "deep anchored convolutional neural networks"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "learning multiple visual domains with residual adapters"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "sharesnet reducing residual network parameter number by sharing weights"
    - "deep networks with stochastic depth"
    - "eie efficient inference engine on compressed deep neural network"
    - "wide residual networks"

Connected through hyperedge HE-8 (induced by source paper "relay a high level ir for deep learning"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "don t unroll adjoint differentiating ssa form programs"
    - "glow graph lowering compiler techniques for neural networks"
    - "darts differentiable architecture search"
    - "neural ordinary differential equations"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "tangent automatic differentiation using source code transformation in python"
    - "in datacenter performance analysis of a tensor processing unit"

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)