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 "computation scheduling for distributed machine learning with straggling workers"):
  Abstract: We study scheduling of computation tasks across   $n$   workers in a large scale distributed learning problem with the help of a master. Computation and communication delays are assumed to be random, and redundant computations are assigned to workers in order to tolerate stragglers. We consider sequential computation of tasks assigned to a worker, while the result of each computation is sent to the master right after its completion. Each computation round, which can model an iteration of the stochastic gradient descent (SGD) algorithm, is  completed  once the master receives   $k$   distinct computations, referred to as the  computation target . Our goal is to characterize the  average completion time  as a function of the  computation load , which denotes the portion of the dataset available at each worker, and the computation target. We propose two computation scheduling schemes that specify the tasks assigned to each worker, as well as their computation schedule, i.e., the order of execution. Assuming a general statistical model for computation and communication delays, we derive the average completion time of the proposed schemes. We also establish a lower bound on the minimum average completion time by assuming prior knowledge of the random delays. Experimental results carried out on Amazon EC2 cluster show a significant reduction in the average completion time over existing coded and uncoded computing schemes. It is also shown numerically that the gap between the proposed scheme and the lower bound is relatively small, confirming the efficiency of the proposed scheduling design.

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 "collaborative machine learning at the wireless edge with blind transmitters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis")
  Member "federated learning over wireless fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wireless federated learning with local differential privacy")
  Member "hierarchical coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a locality based approach for coded computation")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "sparse binary compression towards distributed deep learning with minimal communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "speeding up distributed gradient descent by utilizing non persistent stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "timely distributed computation with stragglers")

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)