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: "pose estimation for non cooperative spacecraft rendezvous using convolutional neural networks"
  Abstract: On-board estimation of the pose of an uncooperative target spacecraft is an essential task for future on-orbit servicing and close-proximity formation flying missions. However, two issues hinder reliable on-board monocular vision based pose estimation: robustness to illumination conditions due to a lack of reliable visual features and scarcity of image datasets required for training and benchmarking. To address these two issues, this work details the design and validation of a monocular vision based pose determination architecture for spaceborne applications. The primary contribution to the state-of-the-art of this work is the introduction of a novel pose determination method based on Convolutional Neural Networks (CNN) to provide an initial guess of the pose in real-time on-board. The method involves discretizing the pose space and training the CNN with images corresponding to the resulting pose labels. Since reliable training of the CNN requires massive image datasets and computational resources, the parameters of the CNN must be determined prior to the mission with synthetic imagery. Moreover, reliable training of the CNN requires datasets that appropriately account for noise, color, and illumination characteristics expected in orbit. Therefore, the secondary contribution of this work is the introduction of an image synthesis pipeline, which is tailored to generate high fidelity images of any spacecraft 3D model. The proposed technique is scalable to spacecraft of different structural and physical properties as well as robust to the dynamic illumination conditions of space. Through metrics measuring classification and pose accuracy, it is shown that the presented architecture has desirable robustness and scalable properties.

Connected through hyperedge HE-1 (induced by source paper "assistive relative pose estimation for on orbit assembly using convolutional neural networks"):
  with members:
    - "deep learning for spacecraft pose estimation from photorealistic rendering"
    - "pose estimation for non cooperative rendezvous using neural networks"
    - "satellite pose estimation challenge dataset competition design and results"
    - "towards robust learning based pose estimation of noncooperative spacecraft"
    - "geometric loss functions for camera pose regression with deep learning"
    - "occlusion aware object localization segmentation and pose estimation"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-2 (induced by source paper "towards robust learning based pose estimation of noncooperative spacecraft"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose estimation for non cooperative rendezvous using neural networks"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "estimating 6d pose from localizing designated surface keypoints"

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"
    - "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"
    - "designing deep convolutional neural networks for continuous object orientation estimation"
    - "fast r cnn"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

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)