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: "learnable histogram statistical context features for deep neural networks"
  Abstract: Statistical features, such as histogram, Bag-of-Words (BoW) and Fisher Vector, were commonly used with hand-crafted features in conventional classification methods, but attract less attention since the popularity of deep learning methods. In this paper, we propose a learnable histogram layer, which learns histogram features within deep neural networks in end-to-end training. Such a layer is able to back-propagate (BP) errors, learn optimal bin centers and bin widths, and be jointly optimized with other layers in deep networks during training. Two vision problems, semantic segmentation and object detection, are explored by integrating the learnable histogram layer into deep networks, which show that the proposed layer could be well generalized to different applications. In-depth investigations are conducted to provide insights on the newly introduced layer.

Connected through hyperedge HE-1 (induced by source paper "histogram layers for texture analysis"):
  with members:
    - "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"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "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 "interpretable bow networks for adversarial example detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deep attentional structured representation learning for visual recognition"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "improved training of wasserstein gans"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "harmonic unpaired image to image translation"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-4 (induced by source paper "discriminative structural graph classification"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "on the universality of invariant networks"
    - "fast graph representation learning with pytorch geometric"
    - "on the transferability of spectral graph filters"
    - "learning structural node embeddings via diffusion wavelets"
    - "how powerful are graph neural networks"
    - "compressive embedding and visualization using graphs"

Connected through hyperedge HE-5 (induced by source paper "deep distributional sequence embeddings based on a wasserstein loss"):
  with members:
    - "deep eyedentification biometric identification using micro movements of the eye"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "deep metric learning with angular loss"
    - "deep speaker an end to end neural speaker embedding system"
    - "hard aware deeply cascaded embedding"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning with a wasserstein loss"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-6 (induced by source paper "learning beyond human expertise with generative models for dental restorations"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "pixcolor pixel recursive colorization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "pixel level domain transfer"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "energy based generative adversarial network"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-7 (induced by source paper "learning deep representations for scene labeling with semantic context guided supervision"):
  with members:
    - "region based semantic segmentation with end to end training"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "modelling the scene dependent imaging in cameras with a deep neural network"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "learning representations for automatic colorization"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "colorful image colorization"
    - "pixelnet towards a general pixel level architecture"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)