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: "orbslam based endoscope tracking and 3d reconstruction"
  Abstract: We aim to track the endoscope location inside the surgical scene and provide 3D reconstruction, in real-time, from the sole input of the image sequence captured by the monocular endoscope. This information offers new possibilities for developing surgical navigation and augmented reality applications. The main benefit of this approach is the lack of extra tracking elements which can disturb the surgeon performance in the clinical routine. It is our first contribution to exploit ORBSLAM, one of the best performing monocular SLAM algorithms, to estimate both of the endoscope location, and 3D structure of the surgical scene. However, the reconstructed 3D map poorly describe textureless soft organ surfaces such as liver. It is our second contribution to extend ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental results on in-vivo pigs, shows a robust endoscope tracking even with organs deformations and partial instrument occlusions. It also shows the reconstruction density, and accuracy against ground truth surface obtained from CT.

Connected through hyperedge HE-1 (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"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "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-2 (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"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "six degree of freedom localization of endoscopic capsule robots using recurrent neural networks embedded into a convolutional neural network"
    - "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Connected through hyperedge HE-4 (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"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "video based computer aided arthroscopy for patient specific reconstruction of the anterior cruciate ligament"):
  with members:
    - "3d registration of curves and surfaces using local differential information"

Connected through hyperedge HE-6 (induced by source paper "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"):
  with members:
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "real time geometry aware augmented reality in minimally invasive surgery"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "a non rigid map fusion based direct slam method for endoscopic capsule robots"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"

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)