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 "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.

Includes members:
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "query guided regression network with context policy for phrase grounding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "colorization as a proxy task for visual understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rgb based semantic segmentation using self supervised depth pre training")
  Member "time contrastive networks self supervised learning from video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "weakly supervised visual grounding of phrases with linguistic structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")
  Member "learning representations for automatic colorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "grounding of textual phrases in images by reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning features by watching objects move":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)