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 "reconstructing sinus anatomy from endoscopic video towards a radiation free approach for quantitative longitudinal assessment"):
  Abstract: Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes. We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos. We demonstrate the effectiveness and accuracy of our method on in and ex vivo data where we compare to sparse reconstructions from Structure from Motion, dense reconstruction from COLMAP, and ground truth anatomy from CT. Our textured reconstructions are watertight and enable measurement of clinically relevant parameters in good agreement with CT. The source code will be made publicly available upon publication.

Includes members:
  Member "extremely dense point correspondences using a learned feature descriptor":
    also connected through other hyperedges:
      (none)
  Member "defslam tracking and mapping of deforming scenes from monocular sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extremely dense point correspondences using a learned feature descriptor")
  Member "dense depth estimation in monocular endoscopy with self supervised learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leveraging vision and kinematics data to improve realism of biomechanic soft tissue simulation for robotic surgery")
  Member "recurrent neural network for un supervised learning of monocular videovisual odometry and depth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  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-4 (induced by source paper "extremely dense point correspondences using a learned feature descriptor")
  Member "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)