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: "exploring convolutional networks for end to end visual servoing"
  Abstract: Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.

Connected through hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geometric loss functions for camera pose regression with deep learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-2 (induced by source paper "dfvs deep flow guided scene agnostic image based visual servoing"):
  with members:
    - "siamese convolutional neural network for sub millimeter accurate camera pose estimation and visual servoing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "high quality monocular depth estimation via transfer learning"
    - "learning visual servoing with deep features and fitted q iteration"
    - "sim2real view invariant visual servoing by recurrent control"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "siamese convolutional neural network for sub millimeter accurate camera pose estimation and visual servoing"):
  with members:
    - "regnet multimodal sensor registration using deep neural networks"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to track at 100 fps with deep regression networks"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning to see by moving"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach for robust corridor following"):
  with members:
    - "learning visual servoing with deep features and fitted q iteration"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"):
  with members:
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "joint semantic and motion segmentation for dynamic scenes using deep convolutional networks"
    - "edge slam edge points based monocular visual slam"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-6 (induced by source paper "epipolar geometry based learning of multi view depth and ego motion from monocular sequences"):
  with members:
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "digging into self supervised monocular depth estimation"
    - "joint semantic and motion segmentation for dynamic scenes using deep convolutional networks"
    - "edge slam edge points based monocular visual slam"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"

Connected through hyperedge HE-7 (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"
    - "learning to fly by crashing"
    - "visual servoing from deep neural networks"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

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)