human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  Abstract: Matching clothing images from customers and online shopping stores has rich applications in E-commerce. Existing algorithms encoded an image as a global feature vector and performed retrieval with the global representation. However, discriminative local information on clothes are submerged in this global representation, resulting in sub-optimal performance. To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales. The similarity pyramid is represented by a Graph of similarity, where nodes represent similarities between clothing components at different scales, and the final matching score is obtained by message passing along edges. In GRNet, graph reasoning is solved by training a graph convolutional network, enabling to align salient clothing components to improve clothing retrieval. To facilitate future researches, we introduce a new benchmark FindFashion, containing rich annotations of bounding boxes, views, occlusions, and cropping. Extensive experiments show that GRNet obtains new state-of-the-art results on two challenging benchmarks, e.g., pushing the top-1, top-20, and top-50 accuracies on DeepFashion to 26%, 64%, and 75% (i.e., 4%, 10%, and 10% absolute improvements), outperforming competitors with large margins. On FindFashion, GRNet achieves considerable improvements on all empirical settings.

Includes members:
  Member "modanet a large scale street fashion dataset with polygon annotations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural data server a large scale search engine for transfer learning data")
  Member "netizen style commenting on fashion photos dataset and diversity measures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating diverse and informative natural language fashion feedback")
  Member "end to end deep kronecker product matching for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "video2shop exactly matching clothes in videos to online shopping images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving the learning of multi column convolutional neural network for crowd counting")
  Member "deep reasoning with knowledge graph for social relationship understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hicore visual hierarchical context reasoning")
  Member "deep randomized ensembles for metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "improving the annotation of deepfashion images for fine grained attribute recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cooperative embeddings for instance attribute and category retrieval")
  Member "person re identification with deep similarity guided graph neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")

Question: Please tell me which class the source paper 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)