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: "improving the annotation of deepfashion images for fine grained attribute recognition"
  Abstract: DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often used for clothes recognition and although it provides comprehensive annotations, the attributes distribution is unbalanced and repetitive specially for training fine-grained attribute recognition models. In this work, we tailored DeepFashion for fine-grained attribute recognition task by focusing on each category separately. After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes. We use a bilinear convolutional neural network with pairwise ranking loss function for multi-label fine-grained attribute recognition and show that the new annotations improve the results for such a task. The detailed annotations for each of the selected categories are provided for public use.

Connected through hyperedge HE-1 (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"
    - "person re identification with deep similarity guided graph neural network"
    - "videos as space time region graphs"

Connected through hyperedge HE-2 (induced by source paper "cooperative embeddings for instance attribute and category retrieval"):
  with members:
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "in defense of the triplet loss for person re identification"
    - "automatic spatially aware fashion concept discovery"
    - "no fuss distance metric learning using proxies"
    - "learning fashion compatibility with bidirectional lstms"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "adaptive semantic visual tree for hierarchical embeddings"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "video person re identification by temporal residual learning"
    - "attribute aware attention model for fine grained representation learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "beyond triplet loss a deep quadruplet network 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)