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: "pose estimation of periacetabular osteotomy fragments with intraoperative x ray navigation"
  Abstract: Objective:  State-of-the-art navigation systems for pelvic osteotomies use optical systems with external fiducials. In this paper, we propose the use of X-ray navigation for pose estimation of periacetabular fragments without fiducials.  Methods:  A two-dimensional/three-dimensional (2-D/3-D) registration pipeline was developed to recover fragment pose. This pipeline was tested through an extensive simulation study and six cadaveric surgeries. Using osteotomy boundaries in the fluoroscopic images, the preoperative plan was refined to more accurately match the intraoperative shape.  Results:  In simulation, average fragment pose errors were 1.3   ${^\circ }$  /1.7 mm when the planned fragment matched the intraoperative fragment, 2.2   ${^\circ }$  /2.1 mm when the plan was not updated to match the true shape, and 1.9   ${^\circ }$  /2.0 mm when the fragment shape was intraoperatively estimated. In cadaver experiments, the average pose errors were 2.2   ${^\circ }$  /2.2 mm, 3.8   ${^\circ }$  /2.5 mm, and 3.5   ${^\circ }$  /2.2 mm when registering with the actual fragment shape, a preoperative plan, and an intraoperatively refined plan, respectively. Average errors of the lateral center edge angle were less than 2  ${^\circ }$   for all fragment shapes in simulation and cadaver experiments.  Conclusion:  The proposed pipeline is capable of accurately reporting femoral head coverage within a range clinically identified for long-term joint survivability.  Significance:  Human interpretation of fragment pose is challenging and usually restricted to rotation about a single anatomical axis. The proposed pipeline provides an intraoperative estimate of rigid pose with respect to all anatomical axes, is compatible with minimally invasive incisions, and has no dependence on external fiducials.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal aware augmented reality redefining hci in image guided therapy"):
  with members:
    - "reflective ar display an interaction methodology for virtual real alignment in medical robotics"
    - "closing the calibration loop an inside out tracking paradigm for augmented reality in orthopedic surgery"
    - "plan in 2 d execute in 3 d an augmented reality solution for cup placement in total hip arthroplasty"
    - "technical note towards virtual monitors for image guided interventions real time streaming to optical see through head mounted displays"

Connected through hyperedge HE-2 (induced by source paper "automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2d 3d registration"):
  with members:
    - "fast and automatic periacetabular osteotomy fragment pose estimation using intraoperatively implanted fiducials and single view fluoroscopy"
    - "localizing dexterous surgical tools in x ray for image based navigation"
    - "patch based image similarity for intraoperative 2d 3d pelvis registration during periacetabular osteotomy"
    - "fully automatic and real time catheter segmentation in x ray fluoroscopy"
    - "stacked hourglass networks for human pose estimation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "fast and automatic periacetabular osteotomy fragment pose estimation using intraoperatively implanted fiducials and single view fluoroscopy"):
  with members:
    - "pelvis surface estimation from partial ct for computer aided pelvic osteotomies"

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)