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: "incentive design for efficient federated learning in mobile networks a contract theory approach"
  Abstract: To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to join model training have been largely overlooked. The mobile devices suffer from considerable overhead in terms of computation and communication during the federated model training process. Without well-designed incentive, self-interested mobile devices will be unwilling to join federated learning tasks, which hinders the adoption of federated learning. To bridge this gap, in this paper, we adopt the contract theory to design an effective incentive mechanism for simulating the mobile devices with high-quality (i.e., high-accuracy) data to participate in federated learning. Numerical results demonstrate that the proposed mechanism is efficient for federated learning with improved learning accuracy.

Connected through hyperedge HE-1 (induced by source paper "learning to detect malicious clients for robust federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "can you really backdoor federated learning"
    - "semi cyclic stochastic gradient descent"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "federated learning challenges methods and future directions"
    - "federated variance reduced stochastic gradient descent with robustness to byzantine attacks"
    - "federated machine learning concept and applications"
    - "fedmd heterogenous federated learning via model distillation"

Connected through hyperedge HE-2 (induced by source paper "mechanism design for multi party machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "strategyproof linear regression in high dimensions"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "communication efficient learning of deep networks from decentralized data"
    - "truthful linear regression"
    - "auctions with severely bounded communication"

Connected through hyperedge HE-3 (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"
    - "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"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-4 (induced by source paper "toward an automated auction framework for wireless federated learning services market"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "agnostic federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "fair resource allocation in federated learning"
    - "joint service pricing and cooperative relay communication for federated learning"
    - "federated learning with non iid data"
    - "optimal auctions through deep learning"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-5 (induced by source paper "fedmd heterogenous federated learning via model distillation"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "variational federated multi task learning"
    - "towards federated learning at scale system design"
    - "semi cyclic stochastic gradient descent"
    - "federated learning challenges methods and future directions"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"

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)