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: "clothing retrieval with visual attention model"
  Abstract: Clothing retrieval is a challenging problem in computer vision. With the advance of Convolutional Neural Networks (CNNs), the accuracy of clothing retrieval has been significantly improved. FashionNet[1], a recent study, proposes to employ a set of artificial features in the form of landmarks for clothing retrieval, which are shown to be helpful for retrieval. However, the landmark detection module is trained with strong supervision which requires considerable efforts to obtain. In this paper, we propose a self-learning Visual Attention Model (VAM) to extract attention maps from clothing images. The VAM is further connected to a global network to form an end-to-end network structure through Impdrop connection which randomly Dropout on the feature maps with the probabilities given by the attention map. Extensive experiments on several widely used benchmark clothing retrieval data sets have demonstrated the promise of the proposed method. We also show that compared to the trivial Product connection, the Impdrop connection makes the network structure more robust when training sets of limited size are used.

Connected through hyperedge HE-1 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "cbam convolutional block attention module"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "attention is all you need"
    - "automatic spatially aware fashion concept discovery"
    - "learning fashion compatibility with bidirectional lstms"
    - "exploring human like attention supervision in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-3 (induced by source paper "diversity in fashion recommendation using semantic parsing"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning from noisy large scale datasets with minimal supervision"
    - "diversified visual attention networks for fine grained object classification"
    - "deep ten texture encoding network"
    - "using filter banks in convolutional neural networks for texture classification"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "fashion image retrieval with capsule networks"):
  with members:
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "dynamic routing between capsules"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"
    - "hard aware deeply cascaded embedding"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)