human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi objective evolutionary federated learning"):
  Abstract: Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to optimize the structure of the neural network models in federated learning using a multi-objective evolutionary algorithm to simultaneously minimize the communication costs and the global model test errors. A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks. Experimental results on both multilayer perceptrons and convolutional neural networks indicate that the proposed optimization method is able to find optimized neural network models that can not only significantly reduce communication costs but also improve the learning performance of federated learning compared with the standard fully connected neural networks.

Includes members:
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators")
  Member "how to backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trends and advancements in deep neural network communication")
  Member "federated learning with non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "differentially private federated learning a client level perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep learning with elastic averaging sgd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "parallel training of dnns with natural gradient and parameter averaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational federated multi task learning")

Question: Please tell me which class the source paper 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)