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: "3d object instance recognition and pose estimation using triplet loss with dynamic margin"
  Abstract: In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks. Inspired by the descriptor learning approach of Wohlhart et al. [1], we propose a method that introduces the dynamic margin in the manifold learning triplet loss function. Such a loss function is designed to map images of different objects under different poses to a lower-dimensional, similarity-preserving descriptor space on which efficient nearest neighbor search algorithms can be applied. Introducing the dynamic margin allows for faster training times and better accuracy of the resulting low-dimensional manifolds. Furthermore, we contribute the following: adding in-plane rotations (ignored by the baseline method) to the training, proposing new background noise types that help to better mimic realistic scenarios and improve accuracy with respect to clutter, adding surface normals as another powerful image modality representing an object surface leading to better performance than merely depth, and finally implementing an efficient online batch generation that allows for better variability during the training phase. We perform an exhaustive evaluation to demonstrate the effects of our contributions. Additionally, we assess the performance of the algorithm on the large BigBIRD dataset [2] to demonstrate good scalability properties of the pipeline with respect to the number of models.

Connected through hyperedge HE-1 (induced by source paper "6d object pose regression via supervised learning on point clouds"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "open3d a modern library for 3d data processing"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "occlusion resistant object rotation regression from point cloud segments"
    - "when regression meets manifold learning for object recognition and pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "3d pose regression using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot learning for 3d point cloud classification"):
  with members:
    - "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "mitigating the hubness problem for zero shot learning of 3d objects"
    - "representation learning on unit ball with 3d roto translational equivariance"
    - "zero shot learning of 3d point cloud objects"
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "deep multiple instance learning for zero shot image tagging"
    - "domain invariant projection learning for zero shot recognition"

Connected through hyperedge HE-3 (induced by source paper "cornet generic 3d corners for 6d pose estimation of new objects without retraining"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "when regression meets manifold learning for object recognition and pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "unsupervised domain adaptation with adversarial residual transform networks"
    - "ipose instance aware 6d pose estimation of partly occluded objects"

Connected through hyperedge HE-4 (induced by source paper "the rgb d triathlon towards agile visual toolboxes for robots"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "taskonomy disentangling task transfer learning"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "3d pose regression using convolutional neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-5 (induced by source paper "triplet distillation for deep face recognition"):
  with members:
    - "learning student networks via feature embedding"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "deep metric learning with hierarchical triplet loss"
    - "in defense of the triplet loss for person re identification"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-6 (induced by source paper "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"):
  with members:
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "triple generative adversarial nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"

Connected through hyperedge HE-7 (induced by source paper "when regression meets manifold learning for object recognition and pose estimation"):
  with members:
    - "generic 3d representation via pose estimation and matching"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"

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)