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: "transfer learning from synthetic to real images using variational autoencoders for precise position detection"
  Abstract: Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data. However, learning from only synthetic images may not achieve the desired performance in the real world due to a gap between synthetic and real images. We propose a method that transfers learned detection of an object position from a simulation environment to the real world. This method uses only a significantly limited dataset of real images while leveraging a large dataset of synthetic images using variational autoencoders. Additionally, the proposed method consistently performed well in different lighting conditions, in the presence of other distractor objects, and on different backgrounds. Experimental results showed that it achieved accuracy of 1.5mm to 3.5mm on average. Furthermore, we showed how the method can be used in a real-world scenario like a "pick-and-place" robotic task.

Connected through hyperedge HE-1 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-2 (induced by source paper "an annotation saved is an annotation earned using fully synthetic training for object instance detection"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "applying domain randomization to synthetic data for object category detection"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "vr goggles for robots real to sim domain adaptation for visual control"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "sem gan semantically consistent image to image translation"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning testbed for power consumption optimization"):
  with members:
    - "maestrob a robotics framework for integrated orchestration of low level control and high level reasoning"
    - "deep reinforcement learning for high precision assembly tasks"
    - "transforming cooling optimization for green data center via deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"
    - "emergence of locomotion behaviours in rich environments"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "3d convolution on rgb d point clouds for accurate model free object pose estimation"):
  with members:
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "multi view 3d object detection network for autonomous driving"
    - "3d model assisted image 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)