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: "cinic 10 is not imagenet or cifar 10"
  Abstract: In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10. It was compiled by combining CIFAR-10 with images selected and downsampled from the ImageNet database. We present the approach to compiling the dataset, illustrate the example images for different classes, give pixel distributions for each part of the repository, and give some standard benchmarks for well known models. Details for download, usage, and compilation can be found in the associated github repository.

Connected through hyperedge HE-1 (induced by source paper "dhog deep hierarchical object grouping"):
  with members:
    - "on mutual information maximization for representation learning"
    - "deep comprehensive correlation mining for image clustering"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "learning representations by maximizing mutual information across views"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples"):
  with members:
    - "structure matters towards generating transferable adversarial images"
    - "high fidelity extraction of neural network models"
    - "on the robustness of the backdoor based watermarking in deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "xnas neural architecture search with expert advice"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "introduction to online convex optimization"
    - "exploring randomly wired neural networks for image recognition"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "on the convergence of adam and beyond"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "similarity preserving knowledge distillation"):
  with members:
    - "lifelong gan continual learning for conditional image generation"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation by on the fly native ensemble"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "syq learning symmetric quantization for efficient deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "psdnet and dpdnet efficient channel expansion depthwise pointwise depthwise inverted bottleneck block"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "recent advances in convolutional neural network acceleration"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "improving generalization and robustness with noisy collaboration in knowledge distillation"):
  with members:
    - "using videos to evaluate image model robustness"
    - "deep self learning from noisy labels"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"

Connected through hyperedge HE-7 (induced by source paper "decentralized learning of generative adversarial networks from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "cgans with projection discriminator"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "spectral normalization for generative adversarial networks"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "how to backdoor federated learning"

Connected through hyperedge HE-8 (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"
    - "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"
    - "multi task self supervised visual 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)