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 "self supervised monocular depth hints"):
  Abstract: Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. #R##N#Here, we study the problem of ambiguous reprojections in depth prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.

Includes members:
  Member "learning monocular depth estimation infusing traditional stereo knowledge":
    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 "edgestereo an effective multi task learning network for stereo matching and edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain invariant stereo matching networks")
  Member "learning depth with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "single view stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multimodal end to end autonomous driving")

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)