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: "visual servoing from deep neural networks"
  Abstract: We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.

Connected through hyperedge HE-1 (induced by source paper "learning view and target invariant visual servoing for navigation"):
  with members:
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"

Connected through hyperedge HE-2 (induced by source paper "learning pose estimation for uav autonomous navigation and landing using visual inertial sensor data"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "geometric loss functions for camera pose regression with deep learning"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "image matching using sift surf brief and orb performance comparison for distorted images"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-3 (induced by source paper "visual geometric skill inference by watching human demonstration"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "online object and task learning via human robot interaction"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-4 (induced by source paper "robustness via retrying closed loop robotic manipulation with self supervised learning"):
  with members:
    - "stochastic adversarial video prediction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "self supervised visual planning with temporal skip connections"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "exploring convolutional networks for end to end visual servoing"
    - "learning to fly by crashing"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

Connected through hyperedge HE-5 (induced by source paper "transfer learning from synthetic to real images using variational autoencoders for precise position detection"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "sim to real transfer of accurate grasping with eye in hand observations and continuous control"
    - "learning a driving simulator"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning deep object detectors from 3d models"

Connected through hyperedge HE-6 (induced by source paper "transfer learning from synthetic to real images using variational autoencoders for robotic applications"):
  with members:
    - "conditional generation of multi modal data using constrained embedding space mapping"
    - "learning a driving simulator"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "modular deep q networks for sim to real transfer of visuo motor policies"):
  with members:
    - "tuning modular networks with weighted losses for hand eye coordination"
    - "progressive neural networks"
    - "bridging between computer and robot vision through data augmentation a case study on object recognition"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "towards vision based deep reinforcement learning for robotic motion control"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"
    - "adapting deep visuomotor representations with weak pairwise constraints"

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.RO(Robotics)