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: "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
  Abstract: X-ray image guidance enables percutaneous alternatives to complex procedures. Unfortunately, the indirect view onto the anatomy in addition to projective simplification substantially increase the task-load for the surgeon. Additional 3D information such as knowledge of anatomical landmarks can benefit surgical decision making in complicated scenarios. Automatic detection of these landmarks in transmission imaging is challenging since image-domain features characteristic to a certain landmark change substantially depending on the viewing direction. Consequently and to the best of our knowledge, the above problem has not yet been addressed. In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction. To this end, a sequential prediction framework based on convolutional layers is trained on synthetically generated data of the pelvic anatomy to predict 23 landmarks in single X-ray images. View independence is contingent on training conditions and, here, is achieved on a spherical segment covering (120 x 90) degrees in LAO/RAO and CRAN/CAUD, respectively, centered around AP. On synthetic data, the proposed approach achieves a mean prediction error of 5.6 +- 4.5 mm. We demonstrate that the proposed network is immediately applicable to clinically acquired data of the pelvis. In particular, we show that our intra-operative landmark detection together with pre-operative CT enables X-ray pose estimation which, ultimately, benefits initialization of image-based 2D/3D registration.

Connected through hyperedge HE-1 (induced by source paper "exploring partial intrinsic and extrinsic symmetry in 3d medical imaging"):
  with members:
    - "detecting approximate reflection symmetry in a point set using optimization on manifold"
    - "beyond planar symmetry modeling human perception of reflection and rotation symmetries in the wild"
    - "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images"

Connected through hyperedge HE-2 (induced by source paper "pose estimation of periacetabular osteotomy fragments with intraoperative x ray navigation"):
  with members:
    - "patch based image similarity for intraoperative 2d 3d pelvis registration during periacetabular osteotomy"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-3 (induced by source paper "multiview 2d 3d rigid registration via a point of interest network for tracking and triangulation point 2"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "dilated fcn for multi agent 2d 3d medical image registration"
    - "an artificial agent for robust image registration"
    - "fully convolutional siamese networks for object tracking"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "localizing dexterous surgical tools in x ray for image based navigation"):
  with members:
    - "inroads toward robot assisted internal fixation of bone fractures using a bendable medical screw and the curved drilling technique"
    - "concurrent segmentation and localization for tracking of surgical instruments"

Connected through hyperedge HE-5 (induced by source paper "a gentle introduction to deep learning in medical image processing"):
  with members:
    - "towards arbitrary noise augmentation deep learning for sampling from arbitrary probability distributions"
    - "metric driven learning of correspondence weighting for 2 d 3 d image registration"
    - "adversarial patch"
    - "image reconstruction via variational network for real time hand held sound speed imaging"
    - "deriving neural network architectures using precision learning parallel to fan beam conversion"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "image reconstruction by domain transform manifold learning"

Connected through hyperedge HE-6 (induced by source paper "augmented reality based feedback for technician in the loop c arm repositioning"):
  with members:
    - "technical note towards virtual monitors for image guided interventions real time streaming to optical see through head mounted displays"

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)