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: "learning frame models using cnn filters"
  Abstract: The convolutional neural network (ConvNet or CNN) has proven to be very successful in many tasks such as those in computer vision. In this conceptual paper, we study the generative perspective of the discriminative CNN. In particular, we propose to learn the generative FRAME (Filters, Random field, And Maximum Entropy) model using the highly expressive filters pre-learned by the CNN at the convolutional layers. We show that the learning algorithm can generate realistic and rich object and texture patterns in natural scenes. We explain that each learned model corresponds to a new CNN unit at a layer above the layer of filters employed by the model. We further show that it is possible to learn a new layer of CNN units using a generative CNN model, which is a product of experts model, and the learning algorithm admits an EM interpretation with binary latent variables.

Connected through hyperedge HE-1 (induced by source paper "learning energy based spatial temporal generative convnets for dynamic patterns"):
  with members:
    - "videoflow a flow based generative model for video"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "wasserstein introspective neural networks"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "introspective classification with convolutional nets"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-2 (induced by source paper "generalizing energy based generative convnets from particle evolution perspective"):
  with members:
    - "frame revisited an interpretation view based on particle evolution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "wasserstein introspective neural networks"
    - "density estimation using real nvp"
    - "improved techniques for training gans"
    - "deep directed generative models with energy based probability estimation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-3 (induced by source paper "macrocanonical models for texture synthesis"):
  with members:
    - "a survey of exemplar based texture synthesis"
    - "texture synthesis through convolutional neural networks and spectrum constraints"
    - "texture synthesis using shallow convolutional networks with random filters"
    - "controlling perceptual factors in neural style transfer"
    - "texture synthesis with spatial generative adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-4 (induced by source paper "patch redundancy in images a statistical testing framework and some applications"):
  with members:
    - "reducing anomaly detection in images to detection in noise"
    - "texture synthesis using convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "probabilistic non local means"

Connected through hyperedge HE-5 (induced by source paper "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robust imitation of diverse behaviors"
    - "learning from synthetic humans"
    - "emergence of locomotion behaviours in rich environments"
    - "how useful is photo realistic rendering for visual learning"
    - "unrealcv connecting computer vision to unreal engine"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-6 (induced by source paper "learning descriptor networks for 3d shape synthesis and analysis"):
  with members:
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "introspective classification with convolutional nets"
    - "vconv dae deep volumetric shape learning without object labels"
    - "learning a predictable and generative vector representation for objects"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "shapenet an information rich 3d model repository"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "frame revisited an interpretation view based on particle evolution"):
  with members:
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "wasserstein introspective neural networks"
    - "improved techniques for training gans"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"
    - "generative modeling of convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "stylebank an explicit representation for neural image style transfer"):
  with members:
    - "style transfer via texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "a learned representation for artistic style"

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)