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 discriminative representation of convolutional features for indoor scene recognition"
  Abstract: Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities that characterize such scenes. This paper presents a novel approach that exploits rich mid-level convolutional features to categorize indoor scenes. Traditional convolutional features retain the global spatial structure, which is a desirable property for general object recognition. We, however, argue that the structure-preserving property of the convolutional neural network activations is not of substantial help in the presence of large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target data set but also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale data set of 1300 object categories that are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over the previous state-of-the-art approaches on five major scene classification data sets.

Connected through hyperedge HE-1 (induced by source paper "tag based semantic features for scene image classification"):
  with members:
    - "unsupervised deep features for privacy image classification"
    - "indoor image representation by high level semantic features"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "locally supervised deep hybrid model for scene recognition"
    - "places an image database for deep scene understanding"
    - "enriching word vectors with subword information"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-2 (induced by source paper "indoor image representation by high level semantic features"):
  with members:
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "hierarchical deep learning architecture for 10k objects classification"
    - "rethinking the inception architecture for computer vision"
    - "multi scale orderless pooling of deep convolutional activation features"

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"
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "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-4 (induced by source paper "regularization of deep neural networks with spectral dropout"):
  with members:
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "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 "a spatial layout and scale invariant feature representation for indoor scene classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "spatial transformer networks"
    - "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"
    - "automatic discovery and optimization of parts for image classification"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "exemplar based deep discriminative and shareable feature learning for scene image classification"):
  with members:
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "cost sensitive learning of deep feature representations from imbalanced data"):
  with members:
    - "batch normalized maxout network in network"
    - "cost aware pre training for multiclass cost sensitive deep learning"
    - "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"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic 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)