human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "improving image based localization with deep learning the impact of the loss function"):
  Abstract: This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique used when regressing a camera's pose from an image is to formulate the loss as a linear combination of positional and rotational mean squared error (using tuned hyperparameters as coefficients). In this work we observe that changes to rotation and position mutually affect the captured image, and in order to improve performance, a pose regression network's loss function should include a term which combines the error of both of these coupled quantities. Based on task specific observations and experimental tuning, we present said loss term, and create a new model by appending this loss term to the loss function of the pre-existing pose regression network `PoseNet'. We achieve improvements in the localization accuracy of the network for indoor scenes; with decreases of up to 26.7% and 24.0% in the median positional and rotational error respectively, when compared to the default PoseNet.

Includes members:
  Member "deep auxiliary learning for visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "vlocnet deep multitask learning for semantic visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "full frame scene coordinate regression for image based localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical joint scene coordinate classification and regression for visual localization")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "learning less is more 6d camera localization via 3d surface regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "camera relocalization by predicting pairwise relative poses using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the apolloscape open dataset for autonomous driving and its application")
  Member "image based localization using hourglass networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "relative camera pose estimation using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")

Question: Please tell me which class the source paper 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)