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: "virtual training for a real application accurate object robot relative localization without calibration"
  Abstract: Localizing an object accurately with respect to a robot is a key step for autonomous robotic manipulation. In this work, we propose to tackle this task knowing only 3D models of the robot and object in the particular case where the scene is viewed from uncalibrated cameras — a situation which would be typical in an uncontrolled environment, e.g., on a construction site. We demonstrate that this localization can be performed very accurately, with millimetric errors, without using a single real image for training, a strong advantage since acquiring representative training data is a long and expensive process. Our approach relies on a classification Convolutional Neural Network (CNN) trained using hundreds of thousands of synthetically rendered scenes with randomized parameters. To evaluate our approach quantitatively and make it comparable to alternative approaches, we build a new rich dataset of real robot images with accurately localized blocks.

Connected through hyperedge HE-1 (induced by source paper "large scale historical watermark recognition dataset and a new consistency based approach"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "identifying cross depicted historical motifs"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "domain adaptation for visual applications a comprehensive survey"

Connected through hyperedge HE-2 (induced by source paper "estimating 3d motion and forces of person object interactions from monocular video"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "end to end recovery of human shape and pose"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "monte carlo tree search for efficient visually guided rearrangement planning"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "real time object pose estimation with pose interpreter networks"
    - "rearrangement with nonprehensile manipulation using deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "proximal policy optimization algorithms"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "began boundary equilibrium generative adversarial networks"
    - "examining cnn representations with respect to dataset bias"

Connected through hyperedge HE-5 (induced by source paper "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "unrealcv connecting computer vision to unreal engine"

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)