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: "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
  Abstract: In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest. This approach does not only use the CNN classification outcomes, but it also implements voting by exploiting the features produced by the deepest portion of the network. We show that this learning-based segmentation method is robust, multi-region, flexible and can be easily adapted to different modalities. In the attempt to show the capabilities and the behaviour of CNNs when they are applied to medical image analysis, we perform a systematic study of the performances of six different network architectures, conceived according to state-of-the-art criteria, in various situations. We evaluate the impact of both different amount of training data and different data dimensionality (2D, 2.5D and 3D) on the final results. We show results on both MRI and transcranial US volumes depicting respectively 26 regions of the basal ganglia and the midbrain.

Connected through hyperedge HE-1 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-2 (induced by source paper "deep hough voting for 3d object detection in point clouds"):
  with members:
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "semi convolutional operators for instance segmentation"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-3 (induced by source paper "developing brain atlas through deep learning"):
  with members:
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mask r cnn"
    - "a large deformation diffeomorphic approach to registration of clarity images via mutual information"
    - "feature pyramid networks for object detection"
    - "sub cortical brain structure segmentation using f cnn s"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-4 (induced by source paper "automated segmentation of lesions in ultrasound using semi pixel wise cycle generative adversarial nets"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "houghnet neural network architecture for vanishing points detection"):
  with members:
    - "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "robust scene text recognition with automatic rectification"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised white and grey matter segmentation in 3d brain ultrasound"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "cyclical learning rates for training neural networks"
    - "deep neural networks for anatomical brain segmentation"

Connected through hyperedge HE-7 (induced by source paper "meta neural network for realtime and passive deep learning based object recognition"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-8 (induced by source paper "combo loss handling input and output imbalance in multi organ segmentation"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "revisiting small batch training for deep neural networks"
    - "focal loss for dense object detection"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "a survey on deep learning in medical image analysis"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

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)