human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "keypose multi view 3d labeling and keypoint estimation for transparent objects"):
  Abstract: Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Finding the depth of the object is an important part of this task, both for training and prediction, and is usually accomplished with a depth sensor or markers in a motion-capture system. For transparent or highly reflective objects, such methods are not feasible without impinging on the resultant image of the object. Hence, many existing methods restrict themselves to opaque, lambertian objects that give good returns from RGBD sensors. In this paper we address two problems: first, establish an easy method for capturing and labeling 3D keypoints on desktop objects with a stereo sensor (no special depth sensor required); and second, develop a deep method, called $KeyPose$, that learns to accurately predict 3D keypoints on objects, including challenging ones such as transparent objects. To showcase the performance of the method, we create and employ a dataset of 15 clear objects in 5 classes, with 48k 3D-keypoint labeled images. We train both instance and category models, and show generalization to new textures, poses, and objects. KeyPose surpasses state-of-the-art performance in 3D pose estimation on this dataset, sometimes by a wide margin, and even in cases where the competing method is provided with registered depth. We will release a public version of the data capture and labeling pipeline, the transparent object database, and the KeyPose training and evaluation code.

Includes members:
  Member "deepim deep iterative matching for 6d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "amnet deep atrous multiscale stereo disparity estimation networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching")
  Member "poserbpf a rao blackwellized particle filter for 6d object pose tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "kpam keypoint affordances for category level robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "cleargrasp 3d shape estimation of transparent objects for manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "point based multi view stereo network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "normalized object coordinate space for category level 6d object pose and size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "densefusion 6d object pose estimation by iterative dense fusion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")

Question: Please tell me which class the source paper 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)