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: "an artificial agent for robust image registration"
  Abstract: 3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However, this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approaches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategy learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc.) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To cope with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate, on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-art registration methods by a large margin in terms of both accuracy and robustness.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-2 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays"):
  with members:
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "cost effective active learning for deep image classification"
    - "gans for biological image synthesis"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-3 (induced by source paper "neurreg neural registration and its application to image segmentation"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "neural multi scale self supervised registration for echocardiogram dense tracking"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airnet self supervised affine registration for 3d medical images using neural networks"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-4 (induced by source paper "recurrent registration neural networks for deformable image registration"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airlab autograd image registration laboratory"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "dilated fcn for multi agent 2d 3d medical image registration"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "training data independent image registration with gans using transfer learning and segmentation information"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "deep morphological simplification network ms net for guided registration of brain magnetic resonance images"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "adversarial deformation regularization for training image registration neural networks"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "an unsupervised learning model for deformable medical image registration"
    - "quicksilver fast predictive image registration a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "multiview 2d 3d rigid registration via a point of interest network for tracking and triangulation point 2"):
  with members:
    - "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
    - "end to end representation learning for correlation filter based tracking"
    - "dilated fcn for multi agent 2d 3d medical image registration"
    - "fully convolutional siamese networks for object tracking"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (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"
    - "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
    - "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"

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)