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: "predicting slice to volume transformation in presence of arbitrary subject motion"
  Abstract: This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotation and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography and X-ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.

Connected through hyperedge HE-1 (induced by source paper "real time deep pose estimation with geodesic loss for image to template rigid registration"):
  with members:
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "3d pose regression using convolutional neural networks"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "slice to volume medical image registration a survey"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-2 (induced by source paper "computing cnn loss and gradients for pose estimation with riemannian geometry"):
  with members:
    - "geomstats a python package for riemannian geometry in machine learning"
    - "geometric loss functions for camera pose regression with deep learning"
    - "an artificial agent for robust image registration"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "standard plane detection in 3d fetal ultrasound using an iterative transformation network"):
  with members:
    - "sononet real time detection and localisation of fetal standard scan planes in freehand ultrasound"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"):
  with members:
    - "convolutional pose machines"

Connected through hyperedge HE-5 (induced by source paper "3d reconstruction in canonical co ordinate space from arbitrarily oriented 2d images"):
  with members:
    - "slice to volume medical image registration a survey"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a deep metric for multimodal registration"
    - "learning under distributed weak supervision"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "slice to volume medical image registration a survey"):
  with members:
    - "rigid slice to volume medical image registration through markov random fields"
    - "a deep metric for multimodal registration"
    - "fast predictive image registration"
    - "flownet learning optical flow with convolutional networks"
    - "learning to compare image patches via convolutional neural networks"
    - "do convnets learn correspondence"
    - "fully convolutional networks for semantic segmentation"
    - "loopy belief propagation for approximate inference an empirical study"

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)