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: "privacy preserving federated brain tumour segmentation"
  Abstract: Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Although a high-accuracy model could be achieved by appropriately aggregating these model updates, the model shared could indirectly leak the local training examples. In this paper, we investigate the feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup. We implement and evaluate practical federated learning systems for brain tumour segmentation on the BraTS dataset. The experimental results show that there is a trade-off between model performance and privacy protection costs.

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"
    - "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"
    - "federated learning for healthcare informatics"

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"
    - "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"
    - "algorithms and theory for multiple source adaptation"

Connected through hyperedge HE-3 (induced by source paper "practical and bilateral privacy preserving federated learning"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "advances and open problems in federated learning"
    - "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"

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)