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: "locally supervised deep hybrid model for scene recognition"
  Abstract: Convolutional neural networks (CNNs) have recently achieved remarkable successes in various image classification and understanding tasks. The deep features obtained at the top fully connected layer of the CNN (FC-features) exhibit rich global semantic information and are extremely effective in image classification. On the other hand, the convolutional features in the middle layers of the CNN also contain meaningful local information, but are not fully explored for image representation. In this paper, we propose a novel locally supervised deep hybrid model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition. First, we notice that the convolutional features capture local objects and fine structures of scene images, which yield important cues for discriminating ambiguous scenes, whereas these features are significantly eliminated in the highly compressed FC representation. Second, we propose a new local convolutional supervision layer to enhance the local structure of the image by directly propagating the label information to the convolutional layers. Third, we propose an efficient Fisher convolutional vector (FCV) that successfully rescues the orderless mid-level semantic information (e.g., objects and textures) of scene image. The FCV encodes the large-sized convolutional maps into a fixed-length mid-level representation, and is demonstrated to be strongly complementary to the high-level FC-features. Finally, both the FCV and FC-features are collaboratively employed in the LS-DHM representation, which achieves outstanding performance in our experiments. It obtains 83.75% and 67.56% accuracies, respectively, on the heavily benchmarked MIT Indoor67 and SUN397 data sets, advancing the state-of-the-art substantially.

Connected through hyperedge HE-1 (induced by source paper "scene based factored attention for image captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "learning to guide decoding for image captioning"
    - "discriminability objective for training descriptive captions"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "improved image captioning via policy gradient optimization of spider"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "semantic fisher scores for task transfer using objects to classify scenes"):
  with members:
    - "compositional model based fisher vector coding for image classification"
    - "label embedding for image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "handcrafted vs deep learning classification for scalable video qoe modeling"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "youtube 8m a large scale video classification benchmark"
    - "xception deep learning with depthwise separable convolutions"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-4 (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"
    - "places an image database for deep scene understanding"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "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-5 (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"
    - "a discriminative representation of convolutional features for indoor 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-6 (induced by source paper "curriculumnet weakly supervised learning from large scale web images"):
  with members:
    - "deep learning is robust to massive label noise"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "foodnet recognizing foods using ensemble of deep networks"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "squeeze and excitation networks"
    - "webvision challenge visual learning and understanding with web data"
    - "webvision database visual learning and understanding from web data"

Connected through hyperedge HE-7 (induced by source paper "from volcano to toyshop adaptive discriminative region discovery for scene recognition"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "scene graph generation by iterative message passing"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "object level context modeling for scene classification with context cnn"
    - "scene recognition with cnns objects scales and dataset bias"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"

Connected through hyperedge HE-8 (induced by source paper "point cloud based place recognition using cnn feature extraction"):
  with members:
    - "deep learning in neural networks"
    - "training deeper convolutional networks with deep supervision"
    - "learning deep features for discriminative localization"
    - "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"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for 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)