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: "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
  Abstract: Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.

Connected through hyperedge HE-1 (induced by source paper "the future of digital health with federated learning"):
  with members:
    - "multi site fmri analysis using privacy preserving federated learning and domain adaptation abide results"
    - "overcoming forgetting in federated learning on non iid data"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "privacy preserving federated brain tumour segmentation"
    - "peer to peer federated learning on graphs"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "federated learning for healthcare informatics"

Connected through hyperedge HE-2 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation"):
  with members:
    - "a benchmark for anomaly segmentation"
    - "mask scoring r cnn"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "an alarm system for segmentation algorithm based on shape model"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-3 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "self supervised learning for cardiac mr image segmentation by anatomical position prediction"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-4 (induced by source paper "observer variation aware medical image segmentation by combining deep learning and surrogate assisted genetic algorithms"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep co training for semi supervised image segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "constrained cnn losses for weakly supervised segmentation"
    - "deeply supervised salient object detection with short connections"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"
    - "deep co training for semi supervised image recognition"
    - "adversarial learning for semi supervised semantic segmentation"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-7 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "scalable neural architecture search for 3d medical image segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-8 (induced by source paper "thickened 2d networks for efficient 3d medical image segmentation"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "compact generalized non local network"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "differentiable learning to normalize via switchable normalization"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung 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)