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 "towards effective device aware federated learning"):
  Abstract: With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means to leave data and computational resources distributed over a large number of nodes (clients) where a central coordinating server aggregates only locally computed updates without knowing the original data. In this work, we extend the FL framework by pushing forward the state the art in the field on several dimensions: (i) unlike the original FedAvg approach relying solely on single criteria (i.e., local dataset size), a suite of domain- and client-specific criteria constitute the basis to compute each local client's contribution, (ii) the multi-criteria contribution of each device is computed in a prioritized fashion by leveraging a priority-aware aggregation operator used in the field of information retrieval, and (iii) a mechanism is proposed for online-adjustment of the aggregation operator parameters via a local search strategy with backtracking. Extensive experiments on a publicly available dataset indicate the merits of the proposed approach compared to standard FedAvg baseline.

Includes members:
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "federated optimization for heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the future of digital health with federated learning")
  Member "leaf a benchmark for federated settings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "how to backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "federated optimization distributed machine learning for on device intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for federated 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)