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: "scene coordinate and correspondence learning for image based localization"
  Abstract: Scene coordinate regression has become an essential part of current camera re-localization methods. Different versions, such as regression forests and deep learning methods, have been successfully applied to estimate the corresponding camera pose given a single input image. In this work, we propose to regress the scene coordinates pixel-wise for a given RGB image by using deep learning. Compared to the recent methods, which usually employ RANSAC to obtain a robust pose estimate from the established point correspondences, we propose to regress confidences of these correspondences, which allows us to immediately discard erroneous predictions and improve the initial pose estimates. Finally, the resulting confidences can be used to score initial pose hypothesis and aid in pose refinement, offering a generalized solution to solve this task.

Connected through hyperedge HE-1 (induced by source paper "hierarchical joint scene coordinate classification and regression for visual localization"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "expert sample consensus applied to camera re localization"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "cascaded parallel filtering for memory efficient image based localization"
    - "lcr net multi person 2d and 3d pose detection in natural images"

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"
    - "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"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-3 (induced by source paper "improving nighttime retrieval based localization"):
  with members:
    - "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"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "semantic visual localization"
    - "lift learned invariant feature transform"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-4 (induced by source paper "efficient condition based representations for long term visual localization"):
  with members:
    - "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"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "semantic visual localization"
    - "fine tuning cnn image retrieval with no human annotation"
    - "lift learned invariant feature transform"

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)