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: "parallel structure from motion from local increment to global averaging"
  Abstract: In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel. Different from the previous methods which drastically simplify the parameters of SfM and sacrifice the accuracy of the final reconstruction, we try to preserve the connectivities among cameras by proposing a camera clustering algorithm to divide a large SfM problem into smaller sub-problems in terms of camera clusters with overlapping. We then exploit a hybrid formulation that applies the relative poses from local incremental SfM into a global motion averaging framework and produce accurate and consistent global camera poses. Our scalable formulation in terms of camera clusters is highly applicable to the whole SfM pipeline including track generation, local SfM, 3D point triangulation and bundle adjustment. We are even able to reconstruct the camera poses of a city-scale data-set containing more than one million high-resolution images with superior accuracy and robustness evaluated on benchmark, Internet, and sequential data-sets.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "graph based parallel large scale structure from motion"):
  with members:
    - "baseline desensitizing in translation averaging"
    - "rotation averaging and strong duality"
    - "large scale sfm with the distributed camera model"
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-3 (induced by source paper "bundle adjustment revisited"):
  with members:
    - "distributed bundle adjustment"

Connected through hyperedge HE-4 (induced by source paper "learning and matching multi view descriptors for registration of point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "learning compact geometric features"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "matterport3d learning from rgb d data in indoor environments"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-5 (induced by source paper "image based camera localization an overview"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "geometric loss functions for camera pose regression with deep learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "robust stereo visual inertial odometry for fast autonomous flight"

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)