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 theoretical analysis of deep neural networks for texture classification"
  Abstract: We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity.

Connected through hyperedge HE-1 (induced by source paper "histogram layers for texture analysis"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "deep texture manifold for ground terrain recognition"
    - "texture classification in extreme scale variations using ganet"
    - "deep ten texture encoding network"
    - "deep residual learning for image recognition"
    - "material recognition in the wild with the materials in context database"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"):
  with members:
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "adversarial deep structural networks for mammographic mass segmentation"

Connected through hyperedge HE-3 (induced by source paper "pcgan char progressively trained classifier generative adversarial networks for classification of noisy handwritten bangla characters"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "pixel level reconstruction and classification for noisy handwritten bangla characters"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "context encoders feature learning by inpainting"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "deepsat a learning framework for satellite imagery"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning using pretrained cnn and associative memory bank"):
  with members:
    - "dense associative memory is robust to adversarial inputs"
    - "dynamic routing between capsules"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "dense associative memory for pattern recognition"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "simultaneous deep transfer across domains and tasks"

Connected through hyperedge HE-5 (induced by source paper "pixel level reconstruction and classification for noisy handwritten bangla characters"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on classification of distorted images with deep convolutional neural networks"
    - "core sampling framework for pixel classification"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deepsat a learning framework for satellite imagery"
    - "how transferable are features in deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-6 (induced by source paper "convolutional neural network on three orthogonal planes for dynamic texture classification"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "texture segmentation with fully convolutional networks"
    - "using filter banks in convolutional neural networks for texture classification"
    - "learning rotation invariant convolutional filters for texture classification"
    - "dynamic texture and scene classification by transferring deep image features"
    - "understanding neural networks through deep visualization"
    - "deep filter banks for texture recognition description and segmentation"
    - "bilinear cnn models for fine grained visual recognition"

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)