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 spatial layout and scale invariant feature representation for indoor scene classification"
  Abstract: Unlike standard object classification, where the image to be classified contains one or multiple instances of the same object, indoor scene classification is quite different since the image consists of multiple distinct objects. Furthermore, these objects can be of varying sizes and are present across numerous spatial locations in different layouts. For automatic indoor scene categorization, large-scale spatial layout deformations and scale variations are therefore two major challenges and the design of rich feature descriptors which are robust to these challenges is still an open problem. This paper introduces a new learnable feature descriptor called “spatial layout and scale invariant convolutional activations” to deal with these challenges. For this purpose, a new convolutional neural network architecture is designed which incorporates a novel “spatially unstructured” layer to introduce robustness against spatial layout deformations. To achieve scale invariance, we present a pyramidal image representation. For feasible training of the proposed network for images of indoor scenes, this paper proposes a methodology, which efficiently adapts a trained network model (on a large-scale data) for our task with only a limited amount of available training data. The efficacy of the proposed approach is demonstrated through extensive experiments on a number of data sets, including MIT-67, Scene-15, Sports-8, Graz-02, and NYU data sets.

Connected through hyperedge HE-1 (induced by source paper "image super resolution as a defense against adversarial attacks"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "video salient object detection via fully convolutional networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"

Connected through hyperedge HE-2 (induced by source paper "joint spatial and layer attention for convolutional networks"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "convolutional networks with adaptive inference graphs"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image based localization using lstms for structured feature correlation"

Connected through hyperedge HE-3 (induced by source paper "adversarial training of variational auto encoders for high fidelity image generation"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "adversarial symmetric variational autoencoder"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "energy based generative adversarial network"
    - "conditional image generation with pixelcnn decoders"
    - "a discriminative representation of convolutional features for indoor scene recognition"

Connected through hyperedge HE-4 (induced by source paper "regularization of deep neural networks with spectral dropout"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-5 (induced by source paper "resfeats residual network based features for image classification"):
  with members:
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-6 (induced by source paper "a discriminative representation of convolutional features for indoor scene recognition"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "exemplar based deep discriminative and shareable feature learning for scene image classification"):
  with members:
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "inverting visual representations with convolutional networks"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "natural scene recognition based on superpixels and deep boltzmann machines"
    - "max margin based discriminative feature learning"
    - "unsupervised feature learning with c svddnet"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"

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)