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: "learning unsupervised visual grounding through semantic self supervision"
  Abstract: Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The simple intuition behind this idea is to encourage the model to localize to regions which can explain some semantic property in the data, in our case, the property being the presence of a concept in a set of images. We present thorough quantitative and qualitative experiments to demonstrate the efficacy of our approach and show a 5.6% improvement over the current state of the art on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and comparable to state-of-art performance on the Flickr30k dataset.

Connected through hyperedge HE-1 (induced by source paper "semi supervised phrase localization in a bidirectional caption image retrieval framework"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "an end to end approach to natural language object retrieval via context aware deep reinforcement learning"
    - "deep metric learning via facility location"
    - "grounding of textual phrases in images by reconstruction"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "top down visual saliency guided by captions"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-2 (induced by source paper "multi level multimodal common semantic space for image phrase grounding"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "grounding visual explanations"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "unsupervised textual grounding linking words to image concepts"

Connected through hyperedge HE-3 (induced by source paper "multilevel language and vision integration for text to clip retrieval"):
  with members:
    - "joint event detection and description in continuous video streams"
    - "modulating early visual processing by language"
    - "objects that sound"
    - "learning two branch neural networks for image text matching tasks"
    - "single shot temporal action detection"
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "bottom up and top down attention for image captioning and visual question answering"

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)