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: "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"
  Abstract: Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. #R##N#Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. #R##N#The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "semantically guided representation learning for self supervised monocular depth"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "3d packing for self supervised monocular depth estimation"
    - "upsnet a unified panoptic segmentation network"
    - "seamless scene segmentation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "panoptic feature pyramid networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "learning depth via interaction"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "unsupervised learning of shape and pose with differentiable point clouds"

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"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "superdepth self supervised super resolved monocular depth estimation"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "3d packing for self supervised monocular depth estimation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "rethinking imagenet pre training"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"):
  with members:
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "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"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"

Connected through hyperedge HE-7 (induced by source paper "instance wise depth and motion learning from monocular videos"):
  with members:
    - "learning residual flow as dynamic motion from stereo videos"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "learning independent object motion from unlabelled stereoscopic videos"
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "video instance segmentation"
    - "visuomotor understanding for representation learning of driving scenes"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-8 (induced by source paper "simvodis simultaneous visual odometry object detection and instance segmentation"):
  with members:
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "fusion volumetric object level slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "digging into self supervised monocular depth estimation"

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)