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 analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
  Abstract: This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin. The challenge was hosted in 2018 at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Granada, Spain. The dataset included over 12,500 images across 3 tasks. 900 users registered for data download, 115 submitted to the lesion segmentation task, 25 submitted to the lesion attribute detection task, and 159 submitted to the disease classification task. Novel evaluation protocols were established, including a new test for segmentation algorithm performance, and a test for algorithm ability to generalize. Results show that top segmentation algorithms still fail on over 10% of images on average, and algorithms with equal performance on test data can have different abilities to generalize. This is an important consideration for agencies regulating the growing set of machine learning tools in the healthcare domain, and sets a new standard for future public challenges in healthcare.

Connected through hyperedge HE-1 (induced by source paper "practical and bilateral privacy preserving federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "privacy preserving federated brain tumour segmentation"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "semi cyclic stochastic gradient descent"
    - "federated learning for healthcare informatics"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-2 (induced by source paper "semi supervised few shot learning for medical image segmentation"):
  with members:
    - "on the texture bias for few shot cnn segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "boosting few shot visual learning with self supervision"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"

Connected through hyperedge HE-3 (induced by source paper "irof a low resource evaluation metric for explanation methods"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "interpretations are useful penalizing explanations to align neural networks with prior knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "aggregating explainability methods for neural networks stabilizes explanations"
    - "embedding human knowledge in deep neural network via attention map"
    - "repair removing representation bias by dataset resampling"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "attention is not explanation"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification"):
  with members:
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "skin lesion analysis towards melanoma detection via end to end deep learning of convolutional neural networks"
    - "ensemble of convolutional neural networks for dermoscopic images classification"
    - "skin lesion diagnosis using ensembles unscaled multi crop evaluation and loss weighting"
    - "data augmentation for skin lesion analysis"
    - "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"

Connected through hyperedge HE-6 (induced by source paper "generating contrastive explanations with monotonic attribute functions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contrastive explanation a structural model approach"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-7 (induced by source paper "a new benchmark for evaluation of cross domain few shot learning"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "diversity with cooperation ensemble methods for few shot classification"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "do better imagenet models transfer better"
    - "meta learning for semi supervised few shot classification"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"

Connected through hyperedge HE-8 (induced by source paper "mixmodule mixed cnn kernel module for medical image segmentation"):
  with members:
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "feature pyramid networks for object detection"

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)