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: "real time image based 6 dof localization in large scale environments"
  Abstract: We present a real-time approach for image-based localization within large scenes that have been reconstructed offline using structure from motion (Sfm). From monocular video, our method continuously computes a precise 6-DOF camera pose, by efficiently tracking natural features and matching them to 3D points in the Sfm point cloud. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the need for online extraction of scale-invariant features. Instead, offline we construct an indexed database containing multiple DAISY descriptors per 3D point extracted at multiple scales. The key to the efficiency of our method lies in invoking DAISY descriptor extraction and matching sparingly during localization, and in distributing this computation over a window of successive frames. This enables the algorithm to run in real-time, without fluctuations in the latency over long durations. We evaluate the method in large indoor and outdoor scenes. Our algorithm runs at over 30 Hz on a laptop and at 12 Hz on a low-power, mobile computer suitable for onboard computation on a quadrotor micro aerial vehicle.

Connected through hyperedge HE-1 (induced by source paper "to learn or not to learn visual localization from essential matrices"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"

Connected through hyperedge HE-2 (induced by source paper "sparse to dense hypercolumn matching for long term visual localization"):
  with members:
    - "lf net learning local features from images"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "improving nighttime retrieval based localization"
    - "scene coordinate and correspondence learning for image based localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "night to day image translation for retrieval based localization"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-3 (induced by source paper "is this the right place geometric semantic pose verification for indoor visual localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "taskonomy disentangling task transfer learning"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "vlase vehicle localization by aggregating semantic edges"
    - "long term visual localization using semantically segmented images"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-4 (induced by source paper "expert sample consensus applied to camera re localization"):
  with members:
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "geometric loss functions for camera pose regression with deep learning"
    - "exploiting points and lines in regression forests for rgb d camera relocalization"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometry aware learning of maps for camera localization"
    - "backtracking regression forests for accurate camera relocalization"
    - "learning to find good correspondences"

Connected through hyperedge HE-5 (induced by source paper "low latency visual slam with appearance enhanced local map building"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "on device scalable image based localization via prioritized cascade search and fast one many ransac"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "selective deep convolutional features for image retrieval"
    - "image based localization using lstms for structured feature correlation"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-7 (induced by source paper "semantically aware attentive neural embeddings for image based visual localization"):
  with members:
    - "deep audio visual speech recognition"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "attention based pyramid aggregation network for visual place recognition"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "night to day image translation for retrieval based localization"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "long term visual localization using semantically segmented images"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-8 (induced by source paper "semantic visual localization"):
  with members:
    - "learning compact geometric features"
    - "deep learning features at scale for visual place recognition"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "segmatch segment based place recognition in 3d point clouds"
    - "lift learned invariant feature transform"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "generic 3d representation via pose estimation and matching"
    - "planet photo geolocation with convolutional neural networks"

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)