human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "slam based quasi dense reconstruction for minimally invasive surgery scenes"
  Abstract: Recovering surgical scene structure in laparoscope surgery is crucial step for surgical guidance and augmented reality applications. In this paper, a quasi dense reconstruction algorithm of surgical scene is proposed. This is based on a state-of-the-art SLAM system, and is exploiting the initial exploration phase that is typically performed by the surgeon at the beginning of the surgery. We show how to convert the sparse SLAM map to a quasi dense scene reconstruction, using pairs of keyframe images and correlation-based featureless patch matching. We have validated the approach with a live porcine experiment using Computed Tomography as ground truth, yielding a Root Mean Squared Error of 4.9mm.

Connected through hyperedge HE-1 (induced by source paper "dense depth estimation in monocular endoscopy with self supervised learning methods"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "self supervised learning for dense depth estimation in monocular endoscopy"
    - "unsupervised learning of depth and ego motion from video"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "an observable time series based slam algorithm for deforming environment"):
  with members:
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "orbslam based endoscope tracking and 3d reconstruction"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "efficient two step optimization for large embedded deformation graph based slam"):
  with members:
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "orbslam based endoscope tracking and 3d reconstruction"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "self supervised dense 3d reconstruction from monocular endoscopic video"):
  with members:
    - "dense depth estimation in monocular endoscopy with self supervised learning methods"
    - "self supervised learning for dense depth estimation in monocular endoscopy"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"

Connected through hyperedge HE-5 (induced by source paper "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"):
  with members:
    - "autonomous scanning for endomicroscopic mosaicing and 3d fusion"
    - "orbslam based endoscope tracking and 3d reconstruction"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning for dense depth estimation in monocular endoscopy"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "deeper depth prediction with fully convolutional residual networks"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "depth map prediction from a single image using a multi scale deep network"

Question: Please tell me which class the center node 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)