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: "convolutional capsule network for classification of breast cancer histology images"
  Abstract: Automatization of the diagnosis of any kind of disease is of great importance and it's gaining speed as more and more deep learning solutions are applied to different problems. One of such computer aided systems could be a decision support too able to accurately differentiate between different types of breast cancer histological images - normal tissue or carcinoma. In this paper authors present a deep learning solution, based on convolutional capsule network for classification of four types of images of breast tissue biopsy when hematoxylin and eusin staining is applied. The cross-validation accuracy was achieved to be 0.87 with equaly high sensitivity.

Connected through hyperedge HE-1 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "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-2 (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"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "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-3 (induced by source paper "use of a capsule network to detect fake images and videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fsgan subject agnostic face swapping and reenactment"
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "domain adaptation in multi channel autoencoder based features for robust face anti spoofing"
    - "deep pixel wise binary supervision for face presentation attack detection"

Connected through hyperedge HE-4 (induced by source paper "bach grand challenge on breast cancer histology images"):
  with members:
    - "improve the performance of transfer learning without fine tuning using dissimilarity based multi view learning for breast cancer histology images"
    - "data augmentation by pairing samples for images classification"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "hierarchical resnext models for breast cancer histology image classification"
    - "context aware learning using transferable features for classification of breast cancer histology images"
    - "ensembling neural networks for digital pathology images classification and segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-5 (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"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "brain tumor type classification via capsule networks"
    - "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-6 (induced by source paper "random capsnet forest model for imbalanced malware type classification task"):
  with members:
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "capsacc an efficient hardware accelerator for capsulenets with data reuse"
    - "fast capsnet for lung cancer screening"
    - "deep transfer learning for static malware classification"
    - "brain tumor type classification via capsule networks"
    - "microsoft malware classification challenge"
    - "deep learning at the shallow end malware classification for non domain experts"
    - "capsule networks against medical imaging data challenges"

Connected through hyperedge HE-7 (induced by source paper "multi kernel capsule network for schizophrenia identification"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "zero shot user intent detection via capsule neural networks"
    - "improving classification rate of schizophrenia using a multimodal multi layer perceptron model with structural and functional mr"
    - "dynamic routing between capsules"

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)