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 "machine learning at the wireless edge distributed stochastic gradient descent over the air"):
  Abstract: We study collaborative machine learning (ML) at the wireless edge, where power and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a remote parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and communicated to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless terminals, referred to as the workers, employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over the underlying wireless multiple access channel (MAC). We then introduce an analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation. In A-DSGD, the workers first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are transmitted directly over the MAC without employing any digital code. Numerical results show that A-DSGD converges much faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also observe that the performance of A-DSGD improves with the number of workers (keeping the total size of the dataset constant), while D-DSGD deteriorates, limiting the ability of the latter in harnessing the computation power of edge devices. The lack of quantization and channel encoding/decoding in A-DSGD further speeds up communication, making it very attractive for low-latency ML applications at the wireless network edge.

Includes members:
  Member "computation scheduling for distributed machine learning with straggling workers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers")
  Member "hierarchical coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a locality based approach for coded computation")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "sparcml high performance sparse communication for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "broadband analog aggregation for low latency federated edge learning extended version":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless federated learning with local differential privacy")
  Member "federated learning via over the air computation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "wireless federated learning with local differential privacy")

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.DC(Distributed, Parallel, and Cluster Computing)