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: "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
  Abstract: Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasing the task complexity because the scene is observed from different view points. The main challenge is the disambiguation of the camera motion from scene motion, which becomes more difficult as the amount of rigidity observed decreases, even with successful estimation of 2D image correspondences. Compared to other state-of-the-art 3D scene flow estimation methods, in this paper we propose to \emph{learn} the rigidity of a scene in a supervised manner from a large collection of dynamic scene data, and directly infer a rigidity mask from two sequential images with depths. With the learned network, we show how we can effectively estimate camera motion and projected scene flow using computed 2D optical flow and the inferred rigidity mask. For training and testing the rigidity network, we also provide a new semi-synthetic dynamic scene dataset (synthetic foreground objects with a real background) and an evaluation split that accounts for the percentage of observed non-rigid pixels. Through our evaluation we show the proposed framework outperforms current state-of-the-art scene flow estimation methods in challenging dynamic scenes.

Connected through hyperedge HE-1 (induced by source paper "flowfusion dynamic dense rgb d slam based on optical flow"):
  with members:
    - "ds slam a semantic visual slam towards dynamic environments"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "optical flow in mostly rigid scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning to refine object segments"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "sense a shared encoder network for scene flow estimation"):
  with members:
    - "deep rigid instance scene flow"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "unified perceptual parsing for scene understanding"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-3 (induced by source paper "learning the depths of moving people by watching frozen people"):
  with members:
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "soccer on your tabletop"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning residual flow as dynamic motion from stereo videos"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "deeptam deep tracking and mapping"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "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"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "path aggregation network for instance segmentation"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-6 (induced by source paper "scene motion decomposition for learnable visual odometry"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "deeptam deep tracking and mapping"
    - "deep ordinal regression network for monocular depth estimation"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "guided feature selection for deep visual odometry"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"

Connected through hyperedge HE-7 (induced by source paper "nrmvs non rigid multi view stereo"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "scalable dense monocular surface reconstruction"
    - "capturing dynamic textured surfaces of moving targets"

Connected through hyperedge HE-8 (induced by source paper "taking a deeper look at the inverse compositional algorithm"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep model based 6d pose refinement in rgb"
    - "open3d a modern library for 3d data processing"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "end to end recovery of human shape and pose"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

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)