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 "an observable time series based slam algorithm for deforming environment"):
  Abstract: In this paper, we study the back-end of simultaneous localization and mapping (SLAM) problem in deforming environment, where robot localizes itself and tracks multiple non-rigid soft surface using its onboard sensor measurements. An elaborate analysis is conducted on conventional deformation modelling method, Embedded Deformation (ED) graph. We demonstrate and prove that the ED graph widely used in such scenarios is unobservable and leads to multiple solutions unless suitable priors are provided. Example as well as theoretical prove are provided to show the ambiguity of ED graph and camera pose. In modelling non-rigid scenario with ED graph, motion priors of the deforming environment is essential to separate robot pose and deforming environment. The conclusion can be extrapolated to any free form deformation formulation. In solving the observability, this research proposes a preliminary deformable SLAM approach to estimate robot pose in complex environments that exhibits regular motion. A strategy that approximates deformed shape using a linear combination of several previous shapes is proposed to avoid the ambiguity in robot movement and rigid and non-rigid motions of the environment. Fisher information matrix rank analysis with a base case is discussed to prove the effectiveness. Moreover, the proposed algorithm is validated extensively on Monte Carlo simulations and real experiments. It is demonstrated that the new algorithm significantly outperforms conventional rigid SLAM and ED based SLAM especially in scenarios where there is large deformation.

Includes members:
  Member "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extremely dense point correspondences using a learned feature descriptor")
  Member "a non rigid map fusion based direct slam method for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structured coupled generative adversarial networks for unsupervised monocular depth estimation")
  Member "slam based quasi dense reconstruction for minimally invasive surgery scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dense depth estimation in monocular endoscopy with self supervised learning methods")
  Member "orbslam based endoscope tracking and 3d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient two step optimization for large embedded deformation graph based slam")
  Member "volumedeform real time volumetric non rigid reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepcap monocular human performance capture using weak supervision")
  Member "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "orb slam a versatile and accurate monocular slam system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")

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)