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 "data importance aware user scheduling for communication efficient edge machine learning"):
  Abstract: With the prevalence of intelligent mobile applications, edge learning is emerging as a promising technology for powering fast intelligence acquisition for edge devices from distributed data generated at the network edge. One critical task of edge learning is to efficiently utilize the limited radio resource to acquire data samples for model training at an edge server. In this paper, we develop a novel user scheduling algorithm for data acquisition in edge learning, called (data) importance-aware scheduling. A key feature of this scheduling algorithm is that it takes into account the informativeness of data samples, besides communication reliability. Specifically, the scheduling decision is based on a data importance indicator (DII), elegantly incorporating two "important" metrics from communication and learning perspectives, i.e., the signal-to-noise ratio (SNR) and data uncertainty. We first derive an explicit expression for this indicator targeting the classic classifier of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. Then, the result is extended to convolutional neural networks (CNN) by replacing the distance based uncertainty measure with the entropy. As demonstrated via experiments using real datasets, the proposed importance-aware scheduling can exploit the two-fold multi-user diversity, namely the diversity in both the multiuser channels and the distributed data samples. This leads to faster model convergence than the conventional scheduling schemes that exploit only a single type of diversity.

Includes members:
  Member "machine learning at the wireless edge distributed stochastic gradient descent over the air":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "optimizing pipelined computation and communication for latency constrained edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on analog gradient descent learning over multiple access fading channels")
  Member "accelerating dnn training in wireless federated edge learning system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "edge intelligence paving the last mile of artificial intelligence with edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse optimization for green edge ai inference")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "semantic effectiveness filtering and control for post 5g wireless connectivity":
    also connected through other hyperedges:
      (none)
  Member "mobile edge intelligence and computing for the internet of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse optimization for green edge ai inference")
  Member "active learning solution on distributed edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards federated learning robustness analytics to data heterogeneity")

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.NI(Networking and Internet Architecture)