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: "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
  Abstract: Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.

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 "os2d one stage one shot object detection by matching anchor features"):
  with members:
    - "ranked list loss for deep metric learning"
    - "one shot object detection with co attention and co excitation"
    - "one shot instance segmentation"
    - "neighbourhood consensus networks"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "lstd a low shot transfer detector for object detection"
    - "end to end representation learning for correlation filter based tracking"
    - "end to end weakly supervised semantic alignment"

Connected through hyperedge HE-3 (induced by source paper "unifying deep local and global features for image search"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "local features and visual words emerge in activations"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "learning with average precision training image retrieval with a listwise loss"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "deeply activated salient region for instance search"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-5 (induced by source paper "learning global and local consistent representations for unsupervised image retrieval via deep graph diffusion networks"):
  with members:
    - "estimating node importance in knowledge graphs using graph neural networks"
    - "neural graph collaborative filtering"
    - "iterative visual reasoning beyond convolutions"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "end to end learning of deep visual representations for image retrieval"
    - "gated graph sequence neural networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"

Connected through hyperedge HE-6 (induced by source paper "memory based neighbourhood embedding for visual recognition"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "person re identification with deep similarity guided graph neural network"
    - "learning embedding adaptation for few shot learning"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "spectral feature transformation for person re identification"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-7 (induced by source paper "graph convolutional networks for learning with few clean and many noisy labels"):
  with members:
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "a closer look at few shot classification"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "iterative learning with open set noisy labels"
    - "probabilistic model agnostic meta learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with large scale diffusion"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-8 (induced by source paper "a probabilistic approach for learning embeddings without supervision"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "hardness aware deep metric learning"
    - "few shot learning with localization in realistic settings"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "deep clustering for unsupervised learning of visual features"
    - "adversarial metric learning"
    - "mining on manifolds metric learning without labels"

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)