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: "reliable federated learning for mobile networks"
  Abstract: Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving traffic flow prediction a federated learning approach"):
  with members:
    - "fedhealth a federated transfer learning framework for wearable healthcare"
    - "towards federated learning at scale system design"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "fair resource allocation in federated learning"
    - "a generic framework for privacy preserving deep learning"

Connected through hyperedge HE-3 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "limited feedback in single and multi user mimo systems with finite bit adcs"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-4 (induced by source paper "ppgan privacy preserving generative adversarial network"):
  with members:
    - "gaussian differential privacy"
    - "differentially private generative adversarial network"
    - "a style based generator architecture for generative adversarial networks"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "improved training of wasserstein gans"
    - "deep models under the gan information leakage from collaborative deep 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.CR(Cryptography and Security)