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: "surfelwarp efficient non volumetric single view dynamic reconstruction"
  Abstract: We contribute a dense SLAM system that takes a live stream of depth images as input and reconstructs non-rigid deforming scenes in real time, without templates or prior models. In contrast to existing approaches, we do not maintain any volumetric data structures, such as truncated signed distance function (TSDF) fields or deformation fields, which are performance and memory intensive. Our system works with a flat point (surfel) based representation of geometry, which can be directly acquired from commodity depth sensors. Standard graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for all central operations: i.e., nearest neighbor maintenance, non-rigid deformation field estimation and fusion of depth measurements. Our pipeline inherently avoids expensive volumetric operations such as marching cubes, volumetric fusion and dense deformation field update, leading to significantly improved performance. Furthermore, the explicit and flexible surfel based geometry representation enables efficient tackling of topology changes and tracking failures, which makes our reconstructions consistent with updated depth observations. Our system allows robots to maintain a scene description with non-rigidly deformed objects that potentially enables interactions with dynamic working environments.

Connected through hyperedge HE-1 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction"):
  with members:
    - "open sourced reinforcement learning environments for surgical robotics"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "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-2 (induced by source paper "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"):
  with members:
    - "open sourced reinforcement learning environments for surgical robotics"
    - "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "robust estimation of similarity transformation for visual object tracking"
    - "robot autonomy for surgery"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "real time 3d tracking of articulated tools for robotic surgery"
    - "dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture"

Connected through hyperedge HE-3 (induced by source paper "surfelmeshing online surfel based mesh reconstruction"):
  with members:
    - "real time cpu based large scale 3d mesh reconstruction"
    - "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection"
    - "efficient online surface correction for real time large scale 3d reconstruction"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "efficient moving point handling for incremental 3d manifold reconstruction"

Connected through hyperedge HE-4 (induced by source paper "dense object nets learning dense visual object descriptors by and for robotic manipulation"):
  with members:
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"
    - "semantic segmentation from limited training data"

Connected through hyperedge HE-5 (induced by source paper "topology aware non rigid point cloud registration"):
  with members:
    - "cilantro a lean versatile and efficient library for point cloud data processing"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "volumedeform real time volumetric non rigid reconstruction"

Connected through hyperedge HE-6 (induced by source paper "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization"):
  with members:
    - "fast and accurate point cloud registration using trees of gaussian mixtures"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "an adaptive data representation for robust point set registration and merging"
    - "point set registration coherent point drift"

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)