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: "multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation"
  Abstract: Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to a centralized location faces various legal, privacy, technical, and data-ownership challenges, especially among international institutions. In this study, we introduce the first use of federated learning for multi-institutional collaboration, enabling deep learning modeling without sharing patient data. Our quantitative results demonstrate that the performance of federated semantic segmentation models (Dice=0.852) on multimodal brain scans is similar to that of models trained by sharing data (Dice=0.862). We compare federated learning with two alternative collaborative learning methods and find that they fail to match the performance of federated learning.

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"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "peer to peer federated learning on graphs"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"

Connected through hyperedge HE-2 (induced by source paper "multi site fmri analysis using privacy preserving federated learning and domain adaptation abide results"):
  with members:
    - "multi source domain adaptation for semantic segmentation"
    - "deep leakage from gradients"
    - "privacy preserving federated brain tumour segmentation"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "capacity bounded differential privacy"
    - "federated adversarial domain adaptation"
    - "private federated learning with domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "federated extra trees with privacy preserving"):
  with members:
    - "federated forest"
    - "local differential privacy for deep learning"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "loadaboost loss based adaboost federated machine learning on medical data"
    - "differential private stack generalization with an application to diabetes prediction"
    - "federated meta learning for recommendation"

Connected through hyperedge HE-4 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "transparency motivations and challenges"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"

Connected through hyperedge HE-5 (induced by source paper "abnormal client behavior detection in federated learning"):
  with members:
    - "privacy preserving deep learning via weight transmission"
    - "towards federated learning at scale system design"
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "federated machine learning concept and applications"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "analyzing federated learning through an adversarial lens"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-6 (induced by source paper "model aggregation via good enough model spaces"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "overcoming catastrophic forgetting in neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "going digital a survey on digitalization and large scale data analytics in healthcare"
    - "communication complexity of distributed convex learning and optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving federated brain tumour segmentation"):
  with members:
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding the sparse vector technique for differential privacy"
    - "communication efficient learning of deep networks from decentralized data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a blockchain orchestrated federated learning architecture for healthcare consortia"):
  with members:
    - "towards federated learning at scale system design"
    - "braintorrent a peer to peer environment for decentralized federated learning"
    - "a generic framework for privacy preserving deep learning"
    - "exploiting unintended feature leakage in collaborative learning"

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.LG(Machine Learning)