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: "eng end to end neural geometry for robust depth and pose estimation using cnns"
  Abstract: Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our motion estimation framework outperforms the previous motion prediction systems and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.

Connected through hyperedge HE-1 (induced by source paper "visual odometry revisited what should be learnt"):
  with members:
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "self supervised learning for single view depth and surface normal estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "pose graph optimization for unsupervised monocular visual odometry"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"

Connected through hyperedge HE-2 (induced by source paper "empnet neural localisation and mapping using embedded memory points"):
  with members:
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "traversing latent space using decision ferns"
    - "cream condensed real time models for depth prediction using convolutional neural networks"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"

Connected through hyperedge HE-3 (induced by source paper "look no deeper recognizing places from opposing viewpoints under varying scene appearance using single view depth estimation"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "a deeper insight into the undemon unsupervised deep network for depth and ego motion estimation"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "x view graph based semantic multi view localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "improving condition and environment invariant place recognition with semantic place categorization"

Connected through hyperedge HE-4 (induced by source paper "scene motion decomposition for learnable visual odometry"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deeptam deep tracking and mapping"
    - "deep ordinal regression network for monocular depth estimation"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "guided feature selection for deep visual odometry"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "learning geometrically consistent mesh corrections"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep depth completion of a single rgb d image"
    - "propagating confidences through cnns for sparse data regression"
    - "meshed up learnt error correction in 3d reconstructions"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "demon depth and motion network for learning monocular stereo"
    - "sparsity invariant cnns"

Connected through hyperedge HE-6 (induced by source paper "cream condensed real time models for depth prediction using convolutional neural networks"):
  with members:
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic 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)