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: "quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks"
  Abstract: This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren \& Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.

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 knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "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"

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"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "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"

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"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "squeeze and excitation networks"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

Connected through hyperedge HE-5 (induced by source paper "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "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"
    - "diversified visual attention networks for fine grained object classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"
    - "recognizing image style"

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)