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: "unsupervised adversarial depth estimation using cycled generative networks"
  Abstract: While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field i.e. the disparity map between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code and trained model are available on this https URL.

Connected through hyperedge HE-1 (induced by source paper "progressive fusion for unsupervised binocular depth estimation using cycled networks"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-2 (induced by source paper "self supervised learning of depth and ego motion with differentiable bundle adjustment"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "ba net dense bundle adjustment network"

Connected through hyperedge HE-3 (induced by source paper "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vision aided absolute trajectory estimation using an unsupervised deep network with online error correction"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "megadepth learning single view depth prediction from internet photos"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-4 (induced by source paper "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-5 (induced by source paper "online adaptation through meta learning for stereo depth estimation"):
  with members:
    - "real time self adaptive deep stereo"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "boosting domain adaptation by discovering latent domains"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-6 (induced by source paper "itergans iterative gans to learn and control 3d object transformation"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "deep hashing network for unsupervised domain adaptation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "generative semantic manipulation with contrasting gan"

Connected through hyperedge HE-7 (induced by source paper "pixel accurate depth evaluation in realistic driving scenarios"):
  with members:
    - "depth coefficients for depth completion"
    - "pixor real time 3d object detection from point clouds"
    - "learning the depths of moving people by watching frozen people"
    - "gated2depth real time dense lidar from gated images"
    - "a benchmark for lidar sensors in fog is detection breaking down"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "semantic foggy scene understanding with synthetic data"
    - "rethinking monocular depth estimation with adversarial training"

Connected through hyperedge HE-8 (induced by source paper "gated2depth real time dense lidar from gated images"):
  with members:
    - "rethinking monocular depth estimation with adversarial training"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "estimating depth from rgb and sparse sensing"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"

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)