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: "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
  Abstract: Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71\% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. We also present attention maps -- given as a class probability distribution -- highlighting the radiological features affecting the network decision. This information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.

Connected through hyperedge HE-1 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "kneel knee anatomical landmark localization using hourglass networks"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "facial landmark detection a literature survey"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "dgc net dense geometric correspondence network"
    - "a general and adaptive robust loss function"
    - "mixup beyond empirical risk minimization"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"

Connected through hyperedge HE-3 (induced by source paper "multimodal machine learning based knee osteoarthritis progression prediction from plain radiographs and clinical data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "squeeze and excitation networks"
    - "quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "automatic instrument segmentation in robot assisted surgery using deep learning"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "angiodysplasia detection and localization using deep convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep convolutional neural networks for breast cancer histology image analysis"):
  with members:
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)