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: "object level deep feature pooling for compact image representation"
  Abstract: Convolutional Neural Network (CNN) features have been successfully employed in recent works as an image descriptor for various vision tasks. But the inability of the deep CNN features to exhibit invariance to geometric transformations and object compositions poses a great challenge for image search. In this work, we demonstrate the effectiveness of the objectness prior over the deep CNN features of image regions for obtaining an invariant image representation. The proposed approach represents the image as a vector of pooled CNN features describing the underlying objects. This representation provides robustness to spatial layout of the objects in the scene and achieves invariance to general geometric transformations, such as translation, rotation and scaling. The proposed approach also leads to a compact representation of the scene, making each image occupy a smaller memory footprint. Experiments show that the proposed representation achieves state of the art retrieval results on a set of challenging benchmark image datasets, while maintaining a compact representation.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-2 (induced by source paper "metalgan a cluster based adaptive training for few shot adversarial colorization"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "on first order meta learning algorithms"
    - "learning to optimize neural nets"
    - "a zero shot framework for sketch based image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learned optimizers that scale and generalize"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "needle in a haystack a framework for seeking small objects in big datasets"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "heated up softmax embedding"
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "region based image retrieval revisited"

Connected through hyperedge HE-4 (induced by source paper "an accurate retrieval through r mac descriptors for landmark recognition"):
  with members:
    - "class weighted convolutional features for visual instance search"
    - "a location aware embedding technique for accurate landmark recognition"
    - "saliency weighted convolutional features for instance search"
    - "context aware query image representation for particular object retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "sift meets cnn a decade survey of instance retrieval"

Connected through hyperedge HE-5 (induced by source paper "a dense depth representation for vlad descriptors in content based image retrieval"):
  with members:
    - "an accurate retrieval through r mac descriptors for landmark recognition"
    - "a location aware embedding technique for accurate landmark recognition"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "exploiting local features from deep networks for image retrieval"

Connected through hyperedge HE-6 (induced by source paper "multi feature fusion for image retrieval using constrained dominant sets"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "deep group shuffling random walk for person re identification"
    - "part based weighting aggregation of deep convolutional features for image retrieval"
    - "dominant sets for constrained image segmentation"
    - "deep image retrieval learning global representations for image search"
    - "interactive image segmentation using constrained dominant sets"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-7 (induced by source paper "deep image representations using caption generators"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "attribute graph a graph based approach to image ranking"
    - "densecap fully convolutional localization networks for dense captioning"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-8 (induced by source paper "a taxonomy of deep convolutional neural nets for computer vision"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "jointly modeling embedding and translation to bridge video and language"
    - "deep learning in neural networks"
    - "vqa visual question answering"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "data free parameter pruning for deep neural networks"
    - "deep convolutional inverse graphics network"

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)