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: "coherent motion segmentation in moving camera videos using optical flow orientations"
  Abstract: In moving camera videos, motion segmentation is commonly performed using the image plane motion of pixels, or optical flow. However, objects that are at different depths from the camera can exhibit different optical flows even if they share the same real-world motion. This can cause a depth-dependent segmentation of the scene. Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene. Our solution uses optical flow orientations instead of the complete vectors and exploits the well-known property that under camera translation, optical flow orientations are independent of object depth. We introduce a probabilistic model that automatically estimates the number of observed independent motions and results in a labeling that is consistent with real-world motion in the scene. The result of our system is that static objects are correctly identified as one segment, even if they are at different depths. Color features and information from previous frames in the video sequence are used to correct occasional errors due to the orientation-based segmentation. We present results on more than thirty videos from different benchmarks. The system is particularly robust on complex background scenes containing objects at significantly different depths.

Connected through hyperedge HE-1 (induced by source paper "moving objects detection with a moving camera a comprehensive review"):
  with members:
    - "deeppbm deep probabilistic background model estimation from video sequences"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "combining background subtraction algorithms with convolutional neural network"
    - "background subtraction in real applications challenges current models and future directions"
    - "learning multi scale features for foreground segmentation"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "are object detection assessment criteria ready for maritime computer vision"

Connected through hyperedge HE-2 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deep declarative networks a new hope"
    - "meta learning with implicit gradients"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"

Connected through hyperedge HE-3 (induced by source paper "spherical formulation of moving object geometric constraints for monocular fisheye cameras"):
  with members:
    - "the double sphere camera model"

Connected through hyperedge HE-4 (induced by source paper "the 2019 davis challenge on vos unsupervised multi object segmentation"):
  with members:
    - "towards segmenting everything that moves"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-5 (induced by source paper "towards segmenting everything that moves"):
  with members:
    - "a structured model for action detection"
    - "object discovery in videos as foreground motion clustering"
    - "videos as space time region graphs"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "youtube vos a large scale video object segmentation benchmark"
    - "learning to segment moving objects"
    - "the 2017 davis challenge on video object segmentation"
    - "learning to segment every thing"

Connected through hyperedge HE-6 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection"):
  with members:
    - "deterministic hypothesis generation for robust fitting of multiple structures"
    - "deterministic consensus maximization with biconvex programming"
    - "dual principal component pursuit"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "high rank matrix completion and subspace clustering with missing data"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "high dimensional matched subspace detection when data are missing"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-7 (induced by source paper "temporally coherent general dynamic scene reconstruction"):
  with members:
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "learning 3d shape completion under weak supervision"
    - "specular to diffuse translation for multi view reconstruction"
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"

Connected through hyperedge HE-8 (induced by source paper "robust video background identification by dominant rigid motion estimation"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "it s moving a probabilistic model for causal motion segmentation in moving camera videos"

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)