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: "real time 3d tracking of articulated tools for robotic surgery"
  Abstract: In robotic surgery, tool tracking is important for providing safe tool-tissue interaction and facilitating surgical skills assessment. Despite recent advances in tool tracking, existing approaches are faced with major difficulties in real-time tracking of articulated tools. Most algorithms are tailored for offline processing with pre-recorded videos. In this paper, we propose a real-time 3D tracking method for articulated tools in robotic surgery. The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation. A robust verification approach is incorporated to reject outliers in 2D detections, which is then followed by fusing inliers with robot kinematic readings for 3D pose estimation of the tool. The proposed method has been validated with phantom data, as well as ex vivo and in vivo experiments. The results derived clearly demonstrate the performance advantage of the proposed method when compared to the state-of-the-art.

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:
    - "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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "single shot pose estimation of surgical robot instruments shafts from monocular endoscopic images"):
  with members:
    - "dynamic active constraints for surgical robots using vector field inequalities"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "a unified framework for the teleoperation of surgical robots in constrained workspaces"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "deep attention based semi supervised 2d pose estimation for surgical instruments"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "group normalization"
    - "modeling visual context is key to augmenting object detection datasets"
    - "recent advances in autoencoder based representation learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "random erasing data augmentation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "concurrent segmentation and localization for tracking of surgical instruments"

Connected through hyperedge HE-4 (induced by source paper "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "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"
    - "dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture"

Connected through hyperedge HE-5 (induced by source paper "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"):
  with members:
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "robot autonomy for surgery"

Connected through hyperedge HE-6 (induced by source paper "concurrent segmentation and localization for tracking of surgical instruments"):
  with members:
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "deeper depth prediction with fully convolutional residual networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "flowing convnets for human pose estimation in videos"

Connected through hyperedge HE-7 (induced by source paper "a vision guided multi robot cooperation framework for learning by demonstration and task reproduction"):
  with members:
    - "autonomous scanning for endomicroscopic mosaicing and 3d fusion"

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)