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 "semi supervised phrase localization in a bidirectional caption image retrieval framework"):
  Abstract: We introduce a novel deep neural network architecture that links visual regions to corresponding textual segments including phrases and words. To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data. From this joint embedding space, we extract the associative localization maps that develop naturally, without explicitly providing supervision during training for the localization task. The joint space is learned using a bidirectional ranking objective that is optimized using a $N$-Pair loss formulation. This training mechanism demonstrates the idea that localization information is learned inherently while optimizing a Bidirectional Retrieval objective. The model's retrieval and localization performance is evaluated on MSCOCO and Flickr30K Entities datasets. This architecture outperforms the state of the art results in the semi-supervised phrase localization setting.

Includes members:
  Member "learning unsupervised visual grounding through semantic self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi level multimodal common semantic space for image phrase grounding")
  Member "multimodal machine learning a survey and taxonomy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "weakly supervised visual grounding of phrases with linguistic structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")
  Member "an end to end approach to natural language object retrieval via context aware deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pirc net using proposal indexing relationships and context for phrase grounding")
  Member "deep metric learning via facility location":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "opengan open set generative adversarial networks")
  Member "grounding of textual phrases in images by reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "phrase localization and visual relationship detection with comprehensive image language cues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual semantic graph attention networks for human object interaction detection")
  Member "top down visual saliency guided by captions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream")

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)