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 "geometry aware symmetric domain adaptation for monocular depth estimation"):
  Abstract: Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to learn depth estimation networks in an unsupervised way by exploring unsupervised cues, which are effective but less reliable than true labels. An emerging way to resolve this dilemma is to transfer knowledge from synthetic images with ground truth depth via domain adaptation techniques. However, these approaches overlook specific geometric structure of the natural images in the target domain (i.e., real data), which is important for high-performing depth prediction. Motivated by the observation, we propose a geometry-aware symmetric domain adaptation framework (GASDA) to explore the labels in the synthetic data and epipolar geometry in the real data jointly. Moreover, by training two image style translators and depth estimators symmetrically in an end-to-end network, our model achieves better image style transfer and generates high-quality depth maps. The experimental results demonstrate the effectiveness of our proposed method and comparable performance against the state-of-the-art. Code will be publicly available at: this https URL.

Includes members:
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "adadepth unsupervised content congruent adaptation for depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "um adapt unsupervised multi task adaptation using adversarial cross task distillation")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "domain adaptive faster r cnn for object detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "t2net synthetic to realistic translation for solving single image depth estimation tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "geonet unsupervised learning of dense depth optical flow and camera pose":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "structured attention guided convolutional neural fields for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "dual cnn models for unsupervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network")

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)