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: "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
  Abstract: Objective:  This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imbalance encountered in real-world multi-class datasets.  Methods:  To use high-resolution images, we propose a novel patch-based attention architecture that provides global context between small, high-resolution patches. We modify three pretrained architectures and study the performance of patch-based attention. To counter class imbalance problems, we compare oversampling, balanced batch sampling, and class-specific loss weighting. Additionally, we propose a novel diagnosis-guided loss weighting method that takes the method used for ground-truth annotation into account.  Results:  Our patch-based attention mechanism outperforms previous methods and improves the mean sensitivity by   $\text{7}\%$  . Class balancing significantly improves the mean sensitivity and we show that our diagnosis-guided loss weighting method improves the mean sensitivity by   $\text{3}\%$   over normal loss balancing.  Conclusion:  The novel patch-based attention mechanism can be integrated into pretrained architectures and provides global context between local patches while outperforming other patch-based methods. Hence, pretrained architectures can be readily used with high-resolution images without downsampling. The new diagnosis-guided loss weighting method outperforms other methods and allows for effective training when facing class imbalance.  Significance:  The proposed methods improve automatic skin lesion classification. They can be extended to other clinical applications where high-resolution image data and class imbalance are relevant.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"
    - "improving semantic segmentation of aerial images using patch based attention"

Connected through hyperedge HE-2 (induced by source paper "a mutual bootstrapping model for automated skin lesion segmentation and classification"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "dilated residual networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"

Connected through hyperedge HE-3 (induced by source paper "skin lesion classification using ensembles of multi resolution efficientnets with meta data"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "skin lesion diagnosis using ensembles unscaled multi crop evaluation and loss weighting"
    - "focal loss for dense object detection"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-4 (induced by source paper "melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "skin lesion diagnosis using ensembles unscaled multi crop evaluation and loss weighting"
    - "multi modal multi scale deep learning for large scale image annotation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)