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: "symmetry aware evaluation of 3d object detection and pose estimation in scenes of many parts in bulk"
  Abstract: While 3D object detection and pose estimation has been studied for a long time, its evaluation is not yet completely satisfactory. Indeed, existing datasets typically consist in numerous acquisitions of only a few scenes because of the tediousness of pose annotation, and existing evaluation protocols cannot handle properly objects with symmetries. This work aims at addressing those two points. We first present automatic techniques to produce fully annotated RGBD data of many object instances in arbitrary poses, with which we produce a dataset of thousands of independent scenes of bulk parts composed of both real and synthetic images. We then propose a consistent evaluation methodology suitable for any rigid object, regardless of its symmetries. We illustrate it with two reference object detection and pose estimation methods on different objects, and show that incorporating symmetry considerations into pose estimation methods themselves can lead to significant performance gains. The proposed dataset is available at http://rbregier.github.io/dataset2017.

Connected through hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image"):
  with members:
    - "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"
    - "dual graph convolutional network for semantic segmentation"
    - "pointrend image segmentation as rendering"
    - "quantized densely connected u nets for efficient landmark localization"
    - "object segmentation in depth maps with one user click and a synthetically trained fully convolutional network"
    - "mvtec d2s densely segmented supermarket dataset"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-2 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bop benchmark for 6d object pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"

Connected through hyperedge HE-3 (induced by source paper "large scale 6d object pose estimation dataset for industrial bin picking"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "defining the pose of any 3d rigid object and an associated distance"
    - "learning dexterous in hand manipulation"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "rgbd datasets past present and future"

Connected through hyperedge HE-4 (induced by source paper "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"):
  with members:
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "geometric loss functions for camera pose regression with deep learning"
    - "relation networks for object detection"
    - "learning non maximum suppression"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "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-5 (induced by source paper "pose estimation for objects with rotational symmetry"):
  with members:
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "siamese regression networks with efficient mid level feature extraction for 3d object pose estimation"
    - "learning descriptors for object recognition and 3d pose estimation"

Connected through hyperedge HE-6 (induced by source paper "jacquard a large scale dataset for robotic grasp detection"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "shapenet an information rich 3d model repository"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-7 (induced by source paper "defining the pose of any 3d rigid object and an associated distance"):
  with members:
    - "posenet a convolutional network for real time 6 dof camera relocalization"

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)