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: "craves controlling robotic arm with a vision based economic system"
  Abstract: Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. #R##N#In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems.

Connected through hyperedge HE-1 (induced by source paper "camera to robot pose estimation from a single image"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "real time 3d traffic cone detection for autonomous driving"
    - "deep high resolution representation learning for human pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "towards visually explaining variational autoencoders"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "sliced wasserstein generative models"
    - "deep learning for anomaly detection a survey"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "re identification with consistent attentive siamese networks"
    - "sharpen focus learning with attention separability and consistency"

Connected through hyperedge HE-3 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "self supervised robot in hand object learning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-4 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep object centric representations for generalizable robot learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"

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)