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 "gradslam dense slam meets automatic differentiation"):
  Abstract: The question of "representation" is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations for SLAM has been an open question, because traditional SLAM systems are not end-to-end differentiable. #R##N#In this work, we present gradSLAM, a differentiable computational graph take on SLAM. Leveraging the automatic differentiation capabilities of computational graphs, gradSLAM enables the design of SLAM systems that allow for gradient-based learning across each of their components, or the system as a whole. This is achieved by creating differentiable alternatives for each non-differentiable component in a typical dense SLAM system. Specifically, we demonstrate how to design differentiable trust-region optimizers, surface measurement and fusion schemes, as well as differentiate over rays, without sacrificing performance. This amalgamation of dense SLAM with computational graphs enables us to backprop all the way from 3D maps to 2D pixels, opening up new possibilities in gradient-based learning for SLAM. #R##N#TL;DR: We leverage the power of automatic differentiation frameworks to make dense SLAM differentiable.

Includes members:
  Member "end to end lane detection through differentiable least squares fitting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "scenecode monocular dense semantic reconstruction using learned encoded scene representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "routedfusion learning real time depth map fusion")
  Member "kornia an open source differentiable computer vision library for pytorch":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "cubeslam monocular 3 d object slam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "generalized inner loop meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning state dependent losses for inverse dynamics learning")
  Member "deeptam deep tracking and mapping":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "regnet learning the optimization of direct image to image pose registration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimizing through learned errors for accurate sports field registration")
  Member "constructing category specific models for monocular object slam":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "not only look but observe variational observation model of scene level 3d multi object understanding for probabilistic slam")

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.RO(Robotics)