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: "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
  Abstract: Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant process recently. Current state-of-the-art (SOTA) methods treat the tasks independently. One important assumption of the current depth estimation pipeline is that the scene contains no moving object, which can be complemented by the optical flow. In this paper, we propose to address the two tasks as a whole, i.e. to jointly understand per-pixel 3D geometry and motion. This also eliminates the need of static scene assumption and enforces the inherent geometrical consistency during the learning process, yielding significantly improved results for both tasks. We call our method as "Every Pixel Counts++" or "EPC++". Specifically, during training, given two consecutive frames from a video, we adopt three parallel networks to predict the camera motion (MotionNet), dense depth map (DepthNet), and per-pixel optical flow between two frames (FlowNet) respectively. Comprehensive experiments were conducted on the KITTI 2012 and KITTI 2015 datasets. Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SOTA methods, demonstrating the effectiveness of each module of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "don t forget the past recurrent depth estimation from monocular video"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "dynamonet dynamic action and motion network"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of camera pose with compositional re estimation"):
  with members:
    - "on the convergence of adam and beyond"
    - "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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "megadepth learning single view depth prediction from internet photos"
    - "ba net dense bundle adjustment network"

Connected through hyperedge HE-3 (induced by source paper "dipe deeper into photometric errors for unsupervised learning of depth and ego motion from monocular videos"):
  with members:
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "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"
    - "superdepth self supervised super resolved monocular depth estimation"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "self supervised deep pose corrections for robust visual odometry"):
  with members:
    - "does computer vision matter for action"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "geometric consistency for self supervised end to end visual odometry"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "a micro lie theory for state estimation in robotics"
    - "dpc net deep pose correction for visual localization"

Connected through hyperedge HE-5 (induced by source paper "occlusion aware unsupervised learning of optical flow from video"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "digging into self supervised monocular depth estimation"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-6 (induced by source paper "fis nets full image supervised networks for monocular depth estimation"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "conf net predicting depth completion error map for high confidence dense 3d point cloud"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "digging into self supervised monocular depth estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-7 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "3d packing for self supervised monocular depth estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "revisiting self supervised visual representation learning"
    - "group normalization"
    - "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"
    - "hartley spectral pooling for deep learning"

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)