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 to real learning to control in visual semantic segmentation"
  Abstract: Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform. Unfortunately, the reality gap between synthetic and real visual data prohibits direct migration of the models trained in virtual worlds to the real world. This paper proposes a modular architecture for tackling the virtual-to-real problem. The proposed architecture separates the learning model into a perception module and a control policy module, and uses semantic image segmentation as the meta representation for relating these two modules. The perception module translates the perceived RGB image to semantic image segmentation. The control policy module is implemented as a deep reinforcement learning agent, which performs actions based on the translated image segmentation. Our architecture is evaluated in an obstacle avoidance task and a target following task. Experimental results show that our architecture significantly outperforms all of the baseline methods in both virtual and real environments, and demonstrates a faster learning curve than them. We also present a detailed analysis for a variety of variant configurations, and validate the transferability of our modular architecture.

Connected through hyperedge HE-1 (induced by source paper "pose assisted multi camera collaboration for active object tracking"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end active object tracking via reinforcement learning"
    - "learning to track at 100 fps with deep regression networks"

Connected through hyperedge HE-2 (induced by source paper "situational fusion of visual representation for visual navigation"):
  with members:
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "driving policy transfer via modularity and abstraction"
    - "learning to navigate in cities without a map"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "learning to navigate from simulation via spatial and semantic information synthesis"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "navigation by imitation in a pedestrian rich environment"
    - "driving policy transfer via modularity and abstraction"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "visual representations for semantic target driven navigation"
    - "virtual to real reinforcement learning for autonomous driving"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"

Connected through hyperedge HE-4 (induced by source paper "invisible marker automatic annotation for object manipulation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "matterport3d learning from rgb d data in indoor environments"
    - "detection and tracking of liquids with fully convolutional networks"
    - "coco stuff thing and stuff classes in context"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "sim to real transfer of robot learning with variable length inputs"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "yolov3 an incremental improvement"
    - "sim to real learning agile locomotion for quadruped robots"
    - "investigating human priors for playing video games"
    - "deep sets"
    - "learning to play general video games via an object embedding network"

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)