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: "slice to volume medical image registration a survey"
  Abstract: During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised learning of metric aggregations for deformable image registration"):
  with members:
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "ssemnet serial section electron microscopy image registration using a spatial transformer network with learned features"
    - "deformable registration through learning of context specific metric aggregation"
    - "a deep metric for multimodal registration"
    - "spatial transformer networks"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "predicting slice to volume transformation in presence of arbitrary subject motion"
    - "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-3 (induced by source paper "3d reconstruction in canonical co ordinate space from arbitrarily oriented 2d images"):
  with members:
    - "predicting slice to volume transformation in presence of arbitrary subject motion"
    - "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-4 (induced by source paper "music transcription by deep learning with data and artificial semantic augmentation"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "an experimental analysis of the entanglement problem in neural network based music transcription systems"
    - "automatized generation of alphabets of symbols"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"

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)