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: "adadepth unsupervised content congruent adaptation for depth estimation"
  Abstract: Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccuracies. While synthetic datasets have been used to circumvent above problems, the resultant models do not generalize well to natural scenes due to the inherent domain shift. Recent adversarial approaches for domain adaption have performed well in mitigating the differences between the source and target domains. But these methods are mostly limited to a classification setup and do not scale well for fully-convolutional architectures. In this work, we propose AdaDepth - an unsupervised domain adaptation strategy for the pixel-wise regression task of monocular depth estimation. The proposed approach is devoid of above limitations through a) adversarial learning and b) explicit imposition of content consistency on the adapted target representation. Our unsupervised approach performs competitively with other established approaches on depth estimation tasks and achieves state-of-the-art results in a semi-supervised setting.

Connected through hyperedge HE-1 (induced by source paper "um adapt unsupervised multi task adaptation using adversarial cross task distillation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "end to end multi task learning with attention"
    - "image to image translation for domain adaptation"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (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"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "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-3 (induced by source paper "unsupervised high resolution depth learning from videos with dual networks"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning visual question answering by bootstrapping hard attention"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-4 (induced by source paper "pattern affinitive propagation across depth surface normal and semantic segmentation"):
  with members:
    - "deep attention based classification network for robust depth prediction"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning depth from monocular videos using synthetic data a temporally consistent domain adaptation approach"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "deep ordinal regression network for monocular depth estimation"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"

Connected through hyperedge HE-6 (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"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "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-7 (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"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "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-8 (induced by source paper "geometry aware symmetric domain adaptation for monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "learning depth from single images with deep neural network embedding focal length"

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)