human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attentive single tasking of multiple tasks")
  Member "every pixel counts unsupervised geometry learning with holistic 3d motion understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multigrid predictive filter flow for unsupervised learning on videos")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "depth estimation via affinity learned with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "geonet unsupervised learning of dense depth optical flow and camera pose":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "lego learning edge with geometry all at once by watching videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")
  Member "digging into self supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")

Question: Please tell me which class the source paper 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)