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 "dsr direct self rectification for uncalibrated dual lens cameras"):
  Abstract: With the developments of dual-lens camera modules,depth information representing the third dimension of thecaptured scenes becomes available for smartphones. It isestimated by stereo matching algorithms, taking as input thetwo views captured by dual-lens cameras at slightly differ-ent viewpoints. Depth-of-field rendering (also be referred toas synthetic defocus or bokeh) is one of the trending depth-based applications. However, to achieve fast depth estima-tion on smartphones, the stereo pairs need to be rectified inthe first place. In this paper, we propose a cost-effective so-lution to perform stereo rectification for dual-lens camerascalled direct self-rectification, short for DSR1. It removesthe need of individual offline calibration for every pair ofdual-lens cameras. In addition, the proposed solution isrobust to the slight movements, e.g., due to collisions, ofthe dual-lens cameras after fabrication. Different with ex-isting self-rectification approaches, our approach computesthe homography in a novel way with zero geometric distor-tions introduced to the master image. It is achieved by di-rectly minimizing the vertical displacements of correspond-ing points between the original master image and the trans-formed slave image. Our method is evaluated on both real-istic and synthetic stereo image pairs, and produces supe-rior results compared to the calibrated rectification or otherself-rectification approaches

Includes members:
  Member "cascade residual learning a two stage convolutional neural network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep depth prior for multi view stereo")
  Member "learning to compare image patches via convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image matching across wide baselines from paper to practice")

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)