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: "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
  Abstract: Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.

Connected through hyperedge HE-1 (induced by source paper "real time federated evolutionary neural architecture search"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "searching for a robust neural architecture in four gpu hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "federated learning strategies for improving communication efficiency"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (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"
    - "privacy preserving federated brain tumour segmentation"
    - "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-3 (induced by source paper "federated continual learning with adaptive parameter communication"):
  with members:
    - "federated learning with matched averaging"
    - "scalable and order robust continual learning with additive parameter decomposition"
    - "asynchronous online federated learning for edge devices"
    - "federated optimization for heterogeneous networks"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"

Connected through hyperedge HE-4 (induced by source paper "free riders in federated learning attacks and defenses"):
  with members:
    - "anomaly detection using autoencoders in high performance computing systems"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "analyzing federated learning through an adversarial lens"
    - "dancing in the dark private multi party machine learning in an untrusted setting"
    - "security and privacy issues in deep learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated 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)