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: "photometric bundle adjustment for vision based slam"
  Abstract: We propose a novel algorithm for the joint refinement of structure and motion parameters from image data directly without relying on fixed and known correspondences. In contrast to traditional bundle adjustment (BA) where the optimal parameters are determined by minimizing the reprojection error using tracked features, the proposed algorithm relies on maximizing the photometric consistency and estimates the correspondences implicitly. Since the proposed algorithm does not require correspondences, its application is not limited to corner-like structure; any pixel with nonvanishing gradient could be used in the estimation process. Furthermore, we demonstrate the feasibility of refining the motion and structure parameters simultaneously using the photometric in unconstrained scenes and without requiring restrictive assumptions such as planarity. The proposed algorithm is evaluated on range of challenging outdoor datasets, and it is shown to improve upon the accuracy of the state-of-the-art VSLAM methods obtained using the minimization of the reprojection error using traditional BA as well as loop closure.

Connected through hyperedge HE-1 (induced by source paper "gn net the gauss newton loss for multi weather relocalization"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "open3d a modern library for 3d data processing"
    - "neighbourhood consensus networks"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "taking a deeper look at the inverse compositional algorithm"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-2 (induced by source paper "flownorm a learning based method for increasing convergence range of direct alignment"):
  with members:
    - "gn net the gauss newton loss for multi weather relocalization"
    - "accurate optical flow via direct cost volume processing"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "direct sparse odometry"
    - "a photometrically calibrated benchmark for monocular visual odometry"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "regnet learning the optimization of direct image to image pose registration"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "aligning across large gaps in time"
    - "deep fundamental matrix estimation without correspondences"
    - "ba net dense bundle adjustment network"
    - "dilated residual networks"
    - "relative camera pose estimation using convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-4 (induced by source paper "cylks unsupervised cycle lucas kanade network for landmark tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "deep lk for efficient adaptive object tracking"
    - "detect to track and track to detect"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "inverse compositional spatial transformer networks"
    - "learning to track at 100 fps with deep regression networks"

Connected through hyperedge HE-5 (induced by source paper "learning depth from monocular videos using direct methods"):
  with members:
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "direct visual odometry using bit planes"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-6 (induced by source paper "object centric photometric bundle adjustment with deep shape prior"):
  with members:
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "shapenet an information rich 3d model repository"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "proxy templates for inverse compositional photometric bundle adjustment"):
  with members:
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "joint epipolar tracking jet simultaneous optimization of epipolar geometry and feature correspondences"):
  with members:
    - "direct sparse odometry"

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)