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 "deep metric learning beyond binary supervision"):
  Abstract: Metric Learning for visual similarity has mostly adopted binary supervision indicating whether a pair of images are of the same class or not. Such a binary indicator covers only a limited subset of image relations, and is not sufficient to represent semantic similarity between images described by continuous and/or structured labels such as object poses, image captions, and scene graphs. Motivated by this, we present a novel method for deep metric learning using continuous labels. First, we propose a new triplet loss that allows distance ratios in the label space to be preserved in the learned metric space. The proposed loss thus enables our model to learn the degree of similarity rather than just the order. Furthermore, we design a triplet mining strategy adapted to metric learning with continuous labels. We address three different image retrieval tasks with continuous labels in terms of human poses, room layouts and image captions, and demonstrate the superior performance of our approach compared to previous methods.

Includes members:
  Member "mining on manifolds metric learning without labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "divide and conquer the embedding space for metric learning")
  Member "beyond triplet loss a deep quadruplet network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "contrastive learning for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comic toward a compact image captioning model with attention")
  Member "self supervised learning of pose embeddings from spatiotemporal relations in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "siamese instance search for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "spice semantic propositional image caption evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)