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: "autogan neural architecture search for generative adversarial networks"
  Abstract: Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and GANs faces its unique challenges. We define the search space for the generator architectural variations and use an RNN controller to guide the search, with parameter sharing and dynamic-resetting to accelerate the process. Inception score is adopted as the reward, and a multi-level search strategy is introduced to perform NAS in a progressive way. Experiments validate the effectiveness of AutoGAN on the task of unconditional image generation. Specifically, our discovered architectures achieve highly competitive performance compared to current state-of-the-art hand-crafted GANs, e.g., setting new state-of-the-art FID scores of 12.42 on CIFAR-10, and 31.01 on STL-10, respectively. We also conclude with a discussion of the current limitations and future potential of AutoGAN. The code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for deep image prior"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "progressive neural architecture search"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"

Connected through hyperedge HE-2 (induced by source paper "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "coco gan generation by parts via conditional coordinating"
    - "few shot unsupervised image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"

Connected through hyperedge HE-3 (induced by source paper "ghostnet more features from cheap operations"):
  with members:
    - "learning instance wise sparsity for accelerating deep models"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "fasterseg searching for faster real time semantic segmentation"
    - "searching for mobilenetv3"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"
    - "searching for accurate binary neural architectures"

Connected through hyperedge HE-4 (induced by source paper "cars continuous evolution for efficient neural architecture search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "progressive darts bridging the optimization gap for nas in the wild"
    - "auto reid searching for a part aware convnet for person re identification"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "learning sparse networks using targeted dropout"
    - "efficient residual dense block search for image super resolution"

Connected through hyperedge HE-5 (induced by source paper "efficient differentiable neural architecture search with meta kernels"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "densely connected search space for more flexible neural architecture search"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"

Connected through hyperedge HE-6 (induced by source paper "adabits neural network quantization with adaptive bit widths"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "differentiable quantization of deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "towards efficient training for neural network quantization"

Connected through hyperedge HE-7 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "adversarialnas adversarial neural architecture search for gans"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "agan towards automated design of generative adversarial networks"
    - "efficient neural architecture transformation searchin channel level for object detection"
    - "one shot neural architecture search via self evaluated template network"
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "deep high resolution representation learning for human pose estimation"

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)