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 "a new method for atlanta world frame estimation"):
  Abstract: In this paper, we propose a new Atlanta frame estimation method by considering the relationship between vertical direction and horizontal directions. Unlike previous solutions, our method does not solve all the directions at one time. On the contrary, it estimates the directions sequentially. Concretely, our method first searches the vertical direction in $\mathbb{S}^2$ globally, then estimates the horizontal directions in one-dimension. As a consequence, the dimensionality of each subproblem problem is low and it can be solved efficiently. In other words, the running time of our method will not greatly increase as the number of horizontal directions increases. The advantages of our method are validated via testing on both synthetic and real-world data.

Includes members:
  Member "robust fitting in computer vision easy or hard":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "geometric multi model fitting with a convex relaxation algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "consac robust multi model fitting by conditional sample consensus")
  Member "multi class model fitting by energy minimization and mode seeking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consac robust multi model fitting by conditional sample consensus")
  Member "efficient global point cloud alignment using bayesian nonparametric mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "can generalised relative pose estimation solve sparse 3d registration")
  Member "robust and globally optimal manhattan frame estimation in near real time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast regularity constrained plane reconstruction")
  Member "go icp a globally optimal solution to 3d icp point set registration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d point cloud processing and learning for autonomous driving")

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)