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: "it s moving a probabilistic model for causal motion segmentation in moving camera videos"
  Abstract: The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer, and even camouflage. In addition to all of this, the ability to detect motion is nearly instantaneous. While there has been much recent progress in motion segmentation, it still appears we are far from human capabilities. In this work, we derive from first principles a new likelihood function for assessing the probability of an optical flow vector given the 3D motion direction of an object. This likelihood uses a novel combination of the angle and magnitude of the optical flow to maximize the information about the true motions of objects. Using this new likelihood and several innovations in initialization, we develop a motion segmentation algorithm that beats current state-of-the-art methods by a large margin. We compare to five state-of-the-art methods on two established benchmarks, and a third new data set of camouflaged animals, which we introduce to push motion segmentation to the next level.

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 "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-3 (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-4 (induced by source paper "robust video background identification by dominant rigid motion estimation"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

Connected through hyperedge HE-5 (induced by source paper "adversarial framework for unsupervised learning of motion dynamics in videos"):
  with members:
    - "video to video synthesis"
    - "video prediction with appearance and motion conditions"
    - "tracking emerges by colorizing videos"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "hierarchical video generation from orthogonal information optical flow and texture"

Connected through hyperedge HE-6 (induced by source paper "adaptive temporal encoding network for video instance level human parsing"):
  with members:
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "visual question reasoning on general dependency tree"
    - "dual motion gan for future flow embedded video prediction"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "flow guided feature aggregation for video object detection"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-7 (induced by source paper "object discovery in videos as foreground motion clustering"):
  with members:
    - "recurrent pixel embedding for instance grouping"
    - "the 2017 davis challenge on video object segmentation"
    - "interactive perception leveraging action in perception and perception in action"
    - "learning video object segmentation with visual memory"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"

Connected through hyperedge HE-8 (induced by source paper "pixel objectness learning to segment generic objects automatically in images and videos"):
  with members:
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "learning video object segmentation with visual memory"
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning motion patterns in videos"
    - "video propagation networks"
    - "what makes for effective detection proposals"
    - "deepsaliency multi task deep neural network model for salient object detection"

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)