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: "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
  Abstract: Existing manifold learning methods are not appropriate for image retrieval task, because most of them are unable to process query image and they have much additional computational cost especially for large scale database. Therefore, we propose the iterative manifold embedding (IME) layer, of which the weights are learned off-line by unsupervised strategy, to explore the intrinsic manifolds by incomplete data. On the large scale database that contains 27000 images, IME layer is more than 120 times faster than other manifold learning methods to embed the original representations at query time. We embed the original descriptors of database images which lie on manifold in a high dimensional space into manifold-based representations iteratively to generate the IME representations in off-line learning stage. According to the original descriptors and the IME representations of database images, we estimate the weights of IME layer by ridge regression. In on-line retrieval stage, we employ the IME layer to map the original representation of query image with ignorable time cost (2 milliseconds). We experiment on five public standard datasets for image retrieval. The proposed IME layer significantly outperforms related dimension reduction methods and manifold learning methods. Without post-processing, Our IME layer achieves a boost in performance of state-of-the-art image retrieval methods with post-processing on most datasets, and needs less computational cost.

Connected through hyperedge HE-1 (induced by source paper "unsupervised semantic based aggregation of deep convolutional features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "residual attention network for image classification"
    - "mask r cnn"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-2 (induced by source paper "an efficient approximate knn graph method for diffusion on image retrieval"):
  with members:
    - "an accurate retrieval through r mac descriptors for landmark recognition"
    - "mining on manifolds metric learning without labels"
    - "low shot learning with large scale diffusion"
    - "end to end learning of deep visual representations for image retrieval"
    - "a survey on learning to hash"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-3 (induced by source paper "mining on manifolds metric learning without labels"):
  with members:
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "polysemous codes"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "multi task self supervised visual learning"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-4 (induced by source paper "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"):
  with members:
    - "recent advance in content based image retrieval a literature survey"
    - "mask r cnn"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "fully convolutional instance aware semantic segmentation"
    - "interactive inter layer activeness propagation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "netvlad cnn architecture for weakly supervised place 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)