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: "packing and padding coupled multi index for accurate image retrieval"
  Abstract: In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. #R##N#Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.

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 "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "interactive inter layer activeness propagation"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-3 (induced by source paper "color recognition for rubik s cube robot"):
  with members:
    - "perceptual visual interactive learning"
    - "learning under distributed weak supervision"
    - "perceptual uniform descriptor and ranking on manifold a bridge between image representation and ranking for image retrieval"

Connected through hyperedge HE-4 (induced by source paper "gesturegan for hand gesture to gesture translation in the wild"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "face aging with contextual generative adversarial nets"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "geometry contrastive gan for facial expression transfer"
    - "cross domain human parsing via adversarial feature and label adaptation"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"

Connected through hyperedge HE-5 (induced by source paper "one shot item search with multimodal data"):
  with members:
    - "deepstyle multimodal search engine for fashion and interior design"
    - "web scale responsive visual search at bing"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "visual search at ebay"
    - "visual discovery at pinterest"
    - "quality resilient deep neural networks"
    - "an empirical study on the effects of different types of noise in image classification tasks"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "query adaptive late fusion for image retrieval"):
  with members:
    - "person recognition in personal photo collections"
    - "unifying identification and context learning for person recognition"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "pseudo positive regularization for deep person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "tag relevance fusion for social image retrieval"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "svdnet for pedestrian retrieval"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"

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)