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: "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
  Abstract: Estimating motion from images is a well-studied problem in computer vision and robotics. Previous work has developed techniques to estimate the motion of a moving camera in a largely static environment (e.g., visual odometry) and to segment or track motions in a dynamic scene using known camera motions (e.g., multiple object tracking). It is more challenging to estimate the unknown motion of the camera and the dynamic scene simultaneously. Most previous work requires a priori object models (e.g., tracking-by-detection), motion constraints (e.g., planar motion), or fails to estimate the full  $SE\ (3)$  motions of the scene (e.g., scene flow). While these approaches work well in specific application domains, they are not generalizable to unconstrained motions. This paper extends the traditional visual odometry (VO) pipeline to estimate the full  $SE\ (3)$  motion of both a stereo/RGB-D camera and the dynamic scene. This multimotion visual odometry (MVO) pipeline requires no a priori knowledge of the environment or the dynamic objects. Its performance is evaluated on a real-world dynamic dataset with ground truth for all motions from a motion capture system.

Connected through hyperedge HE-1 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  with members:
    - "track to reconstruct and reconstruct to track"
    - "stereo based multi motion visual odometry for mobile robots"
    - "unsupervised monocular depth and ego motion learning with structure and semantics"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-2 (induced by source paper "dynamic slam the need for speed"):
  with members:
    - "deep robust single image depth estimation neural network using scene understanding"
    - "cubeslam monocular 3 d object slam"
    - "camera based vehicle velocity estimation from monocular video"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "mask r cnn"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "the oxford multimotion dataset multiple se 3 motions with ground truth"):
  with members:
    - "a white noise on jerk motion prior for continuous time trajectory estimation on se 3"
    - "carla an open urban driving simulator"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-4 (induced by source paper "occlusion robust mvo multimotion estimation through occlusion via motion closure"):
  with members:
    - "the oxford multimotion dataset multiple se 3 motions with ground truth"
    - "a white noise on jerk motion prior for continuous time trajectory estimation on se 3"
    - "geometric multi model fitting with a convex relaxation algorithm"

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.RO(Robotics)