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: "on device scalable image based localization via prioritized cascade search and fast one many ransac"
  Abstract: We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D–3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D–3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows and balconies) in urban localization. Extensive experiments demonstrate that our 2D–3D correspondence search achieves the state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google street view image dataset show the potential of large-scale localization entirely on a typical mobile device.

Connected through hyperedge HE-1 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning"):
  with members:
    - "deep supervised discrete hashing"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "bilinear random projections for locality sensitive binary codes"

Connected through hyperedge HE-2 (induced by source paper "low latency visual slam with appearance enhanced local map building"):
  with members:
    - "good feature selection for least squares pose optimization in vo vslam"
    - "appearance based landmark selection for efficient long term visual localization"
    - "ldso direct sparse odometry with loop closure"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "cascaded parallel filtering for memory efficient image based localization"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "image based localization using lstms for structured feature correlation"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences"):
  with members:
    - "deterministic consensus maximization with biconvex programming"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "alternating direction graph matching"
    - "outlier robust icp for minimizing fractional rmsd"

Connected through hyperedge HE-5 (induced by source paper "visual localization under appearance change a filtering approach"):
  with members:
    - "g2d from gta to data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "semantic visual localization"
    - "geometry aware learning of maps for camera localization"
    - "playing for benchmarks"
    - "image based localization using lstms for structured feature correlation"
    - "dsac differentiable ransac for camera localization"

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)