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: "improved inception residual convolutional neural network for object recognition"
  Abstract: Machine learning and computer vision have driven many of the greatest advances in the modeling of Deep Convolutional Neural Networks (DCNNs). Nowadays, most of the research has been focused on improving recognition accuracy with better DCNN models and learning approaches. The recurrent convolutional approach is not applied very much, other than in a few DCNN architectures. On the other hand, Inception-v4 and Residual networks have promptly become popular among computer the vision community. In this paper, we introduce a new DCNN model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), which utilizes the power of the Recurrent Convolutional Neural Network (RCNN), the Inception network, and the Residual network. This approach improves the recognition accuracy of the Inception-residual network with same number of network parameters. In addition, this proposed architecture generalizes the Inception network, the RCNN, and the Residual network with significantly improved training accuracy. We have empirically evaluated the performance of the IRRCNN model on different benchmarks including CIFAR-10, CIFAR-100, TinyImageNet-200, and CU3D-100. The experimental results show higher recognition accuracy against most of the popular DCNN models including the RCNN. We have also investigated the performance of the IRRCNN approach against the Equivalent Inception Network (EIN) and the Equivalent Inception Residual Network (EIRN) counterpart on the CIFAR-100 dataset. We report around 4.53%, 4.49% and 3.56% improvement in classification accuracy compared with the RCNN, EIN, and EIRN on the CIFAR-100 dataset respectively. Furthermore, the experiment has been conducted on the TinyImageNet-200 and CU3D-100 datasets where the IRRCNN provides better testing accuracy compared to the Inception Recurrent CNN (IRCNN), the EIN, and the EIRN.

Connected through hyperedge HE-1 (induced by source paper "advanced deep convolutional neural network approaches for digital pathology image analysis a comprehensive evaluation with different use cases"):
  with members:
    - "epithelium segmentation using deep learning in h e stained prostate specimens with immunohistochemistry as reference standard"
    - "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction"
    - "automated diagnosis of lymphoma with digital pathology images using deep learning"
    - "a survey on deep learning in medical image analysis"
    - "inception recurrent convolutional neural network for object recognition"
    - "automatic lymphocyte detection in h e images with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "road extraction by deep residual u net"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "a survey on deep learning in medical image analysis"
    - "inception recurrent convolutional neural network for object recognition"

Connected through hyperedge HE-3 (induced by source paper "breast cancer classification from histopathological images with inception recurrent residual convolutional neural network"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a survey on deep learning in medical image analysis"
    - "inception recurrent convolutional neural network for object recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "network in network"

Connected through hyperedge HE-4 (induced by source paper "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"):
  with members:
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "inception recurrent convolutional neural network for object recognition"
    - "deep residual learning for image recognition"
    - "computational pathology challenges and promises for tissue analysis"

Connected through hyperedge HE-5 (induced by source paper "bangla license plate recognition using convolutional neural networks cnn"):
  with members:
    - "inception recurrent convolutional neural network for object recognition"
    - "multi column deep neural networks for image classification"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)