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: "a powerful generative model using random weights for the deep image representation"
  Abstract: To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.

Connected through hyperedge HE-1 (induced by source paper "how powerful are randomly initialized pointcloud set functions"):
  with members:
    - "weight agnostic neural networks"
    - "no training required exploring random encoders for sentence classification"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "learning representations and generative models for 3d point clouds"
    - "instance normalization the missing ingredient for fast stylization"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "error feedback stochastic configuration strategy on convolutional neural networks for time series forecasting"):
  with members:
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "accelerating neural architecture search using performance prediction"
    - "stochastic configuration networks fundamentals and algorithms"
    - "deep stochastic configuration networks with universal approximation property"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "pso mismo modeling strategy for multistep ahead time series prediction"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "weight agnostic neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "continual lifelong learning with neural networks a review"
    - "the evolved transformer"
    - "reinforcement learning for improving agent design"
    - "evaluating the search phase of neural architecture search"
    - "measuring the intrinsic dimension of objective landscapes"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "the hsic bottleneck deep learning without back propagation"):
  with members:
    - "on mutual information maximization for representation learning"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "gradient target propagation"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-5 (induced by source paper "image synthesis and style transfer"):
  with members:
    - "demystifying neural style transfer"
    - "interpreting deep visual representations via network dissection"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "deep learning in neural networks"
    - "texture synthesis using convolutional neural networks"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "securing input data of deep learning inference systems via partitioned enclave execution"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "chiron privacy preserving machine learning as a service"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "efficient deep learning on multi source private data"
    - "the circle game scalable private membership test using trusted hardware"
    - "opening the black box of deep neural networks via information"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "adversarial reprogramming of neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "adversarial logit pairing"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-8 (induced by source paper "synthesizing diverse high quality audio textures"):
  with members:
    - "audio style transfer"
    - "understanding deep representations through random weights"
    - "on using backpropagation for speech texture generation and voice conversion"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "texture synthesis using shallow convolutional networks with random filters"
    - "improved techniques for training gans"
    - "a learned representation for artistic style"
    - "wavenet a generative model for raw audio"

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)