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 efficient solution to non minimal case essential matrix estimation"):
  Abstract: Finding relative pose between two calibrated views is a fundamental task in computer vision. Given the minimal number $5$ of required point correspondences, the classical five-point method can be used to calculate the essential matrix. For the non-minimal cases when $N$ ($N > 5$) correct point correspondences are given, which is called $N$-point problem, methods are relatively less mature. In this paper, we solve the $N$-point problem by minimizing the algebraic error and formulate it as a quadratically constrained quadratic program (QCQP). The formulation is based on a simpler parameterization of the feasible region -- the normalized essential matrix manifold -- than previous approaches. Then a globally optimal solution to this problem is obtained by semidefinite relaxation. This allows us to obtain certifiably global solutions to an important non-convex problem in polynomial time. We provide the condition to recover the optimal essential matrix from the relaxed problems. The theoretical guarantees of the semidefinite relaxation are investigated, including the tightness and local stability. Experiments demonstrate that our approach always finds and certifies (a-posteriori) the global optimum of the cost function, and it is dozens of times faster than state-of-the-art globally optimal solutions.

Includes members:
  Member "equivalent constraints for two view geometry pose solution pure rotation identification and 3d reconstruction":
    also connected through other hyperedges:
      (none)
  Member "deterministic consensus maximization with biconvex programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection")

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)