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: "single image depth estimation trained via depth from defocus cues"
  Abstract: Estimating depth from a single RGB images is a fundamental task in computer vision, which is most directly solved using supervised deep learning. In the field of unsupervised learning of depth from a single RGB image, depth is not given explicitly. Existing work in the field receives either a stereo pair, a monocular video, or multiple views, and, using losses that are based on structure-from-motion, trains a depth estimation network. In this work, we rely, instead of different views, on depth from focus cues. Learning is based on a novel Point Spread Function convolutional layer, which applies location specific kernels that arise from the Circle-Of-Confusion in each image location. We evaluate our method on data derived from five common datasets for depth estimation and lightfield images, and present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches. Since the phenomenon of depth from defocus is not dataset specific, we hypothesize that learning based on it would overfit less to the specific content in each dataset. Our experiments show that this is indeed the case, and an estimator learned on one dataset using our method provides better results on other datasets, than the directly supervised methods.

Connected through hyperedge HE-1 (induced by source paper "self supervised blur detection from synthetically blurred scenes"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "self supervised feature learning by learning to spot artifacts"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-2 (induced by source paper "monster awakening the mono in stereo"):
  with members:
    - "deep optics for monocular depth estimation and 3d object detection"
    - "ga net guided aggregation net for end to end stereo matching"
    - "monocular depth estimation a survey"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "hierarchical discrete distribution decomposition for match density estimation"
    - "pyramid stereo matching network"
    - "deep depth inference using binocular and monocular cues"

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)