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: "designing deep convolutional neural networks for continuous object orientation estimation"
  Abstract: Deep Convolutional Neural Networks (DCNN) have been proven to be effective for various computer vision problems. In this work, we demonstrate its effectiveness on a continuous object orientation estimation task, which requires prediction of 0 to 360 degrees orientation of the objects. We do so by proposing and comparing three continuous orientation prediction approaches designed for the DCNNs. The first two approaches work by representing an orientation as a point on a unit circle and minimizing either L2 loss or angular difference loss. The third method works by first converting the continuous orientation estimation task into a set of discrete orientation estimation tasks and then converting the discrete orientation outputs back to the continuous orientation using a mean-shift algorithm. By evaluating on a vehicle orientation estimation task and a pedestrian orientation estimation task, we demonstrate that the discretization-based approach not only works better than the other two approaches but also achieves state-of-the-art performance. We also demonstrate that finding an appropriate feature representation is critical to achieve a good performance when adapting a DCNN trained for an image recognition task.

Connected through hyperedge HE-1 (induced by source paper "conservative wasserstein training for pose estimation"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "deep verifier networks verification of deep discriminative models with deep generative models"
    - "confidence regularized self training"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "deep directional statistics pose estimation with uncertainty quantification"

Connected through hyperedge HE-2 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-3 (induced by source paper "pose estimation for non cooperative rendezvous using neural networks"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning dexterous in hand manipulation"
    - "pose estimation for non cooperative spacecraft rendezvous using convolutional neural networks"
    - "3d pose regression using convolutional neural networks"
    - "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"
    - "fast r cnn"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-4 (induced by source paper "2 5d image based robotic grasping"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "grasp pose detection in point clouds"
    - "sim2real view invariant visual servoing by recurrent control"

Connected through hyperedge HE-5 (induced by source paper "nostalgin extracting 3d city models from historical image data"):
  with members:
    - "free form image inpainting with gated convolution"
    - "mask r cnn"
    - "semantic image inpainting with deep generative models"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-6 (induced by source paper "3d pose estimation for fine grained object categories"):
  with members:
    - "3d pose regression using convolutional neural networks"
    - "6 dof object pose from semantic keypoints"
    - "the inaturalist species classification and detection dataset"
    - "mask r cnn"
    - "a large scale car dataset for fine grained categorization and verification"
    - "shapenet an information rich 3d model repository"
    - "bilinear cnn models for fine grained visual recognition"
    - "a coarse to fine model for 3d pose estimation and sub category recognition"

Connected through hyperedge HE-7 (induced by source paper "scene understanding networks for autonomous driving based on around view monitoring system"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "understanding convolution for semantic segmentation"
    - "3d object proposals using stereo imagery for accurate object class detection"
    - "3d bounding box estimation using deep learning and geometry"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-8 (induced by source paper "closing the loop for robotic grasping a real time generative grasp synthesis approach"):
  with members:
    - "robotic grasp detection using deep convolutional neural networks"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"

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)