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: "brain tumor type classification via capsule networks"
  Abstract: Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "improved explainability of capsule networks relevance path by agreement"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-3 (induced by source paper "detecting fake news with capsule neural networks"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "information aggregation via dynamic routing for sequence encoding"
    - "zero shot user intent detection via capsule neural networks"
    - "fake news detection in social networks via crowd signals"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "dynamic routing between capsules"
    - "automatic detection of fake news"

Connected through hyperedge HE-4 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"

Connected through hyperedge HE-5 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical cnns on unstructured grids"
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning an effective equivariant 3d descriptor without supervision"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "equivariant neural networks and equivarification"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-6 (induced by source paper "sparsely activated networks a new method for decomposing and compressing data"):
  with members:
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "medical image analysis using convolutional neural networks a review"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "recognizing abnormal heart sounds using deep learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-7 (induced by source paper "building deep equivariant capsule networks"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "equivariant transformer networks"
    - "attention routing between capsules"
    - "the multi lane capsule network mlcn"
    - "context aware multipath networks"
    - "deep learning for classical japanese literature"
    - "group equivariant capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"

Connected through hyperedge HE-8 (induced by source paper "self attention capsule networks for object classification"):
  with members:
    - "quantitative phase imaging and artificial intelligence a review"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"
    - "attention u net learning where to look for the pancreas"
    - "dense and diverse capsule networks making the capsules learn better"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

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)