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: "convex relaxations of se 2 and se 3 for visual pose estimation"
  Abstract: This paper proposes a new method for rigid body pose estimation based on spectrahedral representations of the tautological orbitopes of $SE(2)$ and $SE(3)$. The approach can use dense point cloud data from stereo vision or an RGB-D sensor (such as the Microsoft Kinect), as well as visual appearance data. The method is a convex relaxation of the classical pose estimation problem, and is based on explicit linear matrix inequality (LMI) representations for the convex hulls of $SE(2)$ and $SE(3)$. Given these representations, the relaxed pose estimation problem can be framed as a robust least squares problem with the optimization variable constrained to these convex sets. Although this formulation is a relaxation of the original problem, numerical experiments indicate that it is indeed exact - i.e. its solution is a member of $SE(2)$ or $SE(3)$ - in many interesting settings. We additionally show that this method is guaranteed to be exact for a large class of pose estimation problems.

Connected through hyperedge HE-1 (induced by source paper "pcrnet point cloud registration network using pointnet encoding"):
  with members:
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "fast and accurate point cloud registration using trees of gaussian mixtures"
    - "matching rgb images to cad models for object pose estimation"
    - "iterative transformer network for 3d point cloud"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"

Connected through hyperedge HE-2 (induced by source paper "pointnetlk robust efficient point cloud registration using pointnet"):
  with members:
    - "dynamic graph cnn for learning on point clouds"
    - "a unified framework for multi view multi class object pose estimation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "deep lk for efficient adaptive object tracking"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"

Connected through hyperedge HE-3 (induced by source paper "globally optimal registration of noisy point clouds"):
  with members:
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "outlier robust icp for minimizing fractional rmsd"

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)