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: "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
  Abstract: Real-time simultaneously localization and dense mapping is very helpful for providing Virtual Reality and Augmented Reality for surgeons or even surgical robots. In this paper, we propose MIS-SLAM: a complete real-time large scale dense deformable SLAM system with stereoscope in Minimal Invasive Surgery based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB- SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large scale scope localizing and dense mapping in real-time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose.

Connected through hyperedge HE-1 (induced by source paper "extremely dense point correspondences using a learned feature descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "defslam tracking and mapping of deforming scenes from monocular sequences"
    - "3d reconstruction of whole stomach from endoscope video using structure from motion"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "normface l 2 hypersphere embedding for face verification"
    - "focal loss for dense object detection"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-2 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"
    - "pyramid stereo matching network"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "reconstructing sinus anatomy from endoscopic video towards a radiation free approach for quantitative longitudinal assessment"):
  with members:
    - "extremely dense point correspondences using a learned feature descriptor"
    - "defslam tracking and mapping of deforming scenes from monocular sequences"
    - "dense depth estimation in monocular endoscopy with self supervised learning methods"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "model free vision based shaping of deformable plastic materials"):
  with members:
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "learning robust bed making using deep imitation learning with dart"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "active perception and modeling of deformable surfaces using gaussian processes and position based dynamics"
    - "deep reinforcement learning in large discrete action spaces"

Connected through hyperedge HE-5 (induced by source paper "an observable time series based slam algorithm for deforming environment"):
  with members:
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "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-6 (induced by source paper "efficient two step optimization for large embedded deformation graph based slam"):
  with members:
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "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-7 (induced by source paper "towards safety aware computing system design in autonomous vehicles"):
  with members:
    - "on a formal model of safe and scalable self driving cars"
    - "deep residual learning for image recognition"

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)