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 "stochastic gradient coding for straggler mitigation in distributed learning"):
  Abstract: We consider distributed gradient descent in the presence of stragglers. Recent work on \em gradient coding \em and \em approximate gradient coding \em have shown how to add redundancy in distributed gradient descent to guarantee convergence even if some workers are \em stragglers\em---that is, slow or non-responsive. In this work we propose an approximate gradient coding scheme called \em Stochastic Gradient Coding \em (SGC), which works when the stragglers are random. SGC distributes data points redundantly to workers according to a pair-wise balanced design, and then simply ignores the stragglers. We prove that the convergence rate of SGC mirrors that of batched Stochastic Gradient Descent (SGD) for the $\ell_2$ loss function, and show how the convergence rate can improve with the redundancy. We also provide bounds for more general convex loss functions. We show empirically that SGC requires a small amount of redundancy to handle a large number of stragglers and that it can outperform existing approximate gradient codes when the number of stragglers is large.

Includes members:
  Member "fundamental limits of approximate gradient coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "numerically stable binary gradient coding")
  Member "analyzing the download time of availability codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes")
  Member "distributed stochastic gradient descent using ldgm codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "numerically stable binary gradient coding")
  Member "erasurehead distributed gradient descent without delays using approximate gradient coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a locality based approach for coded computation")
  Member "coded cooperative computation for internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive distributed stochastic gradient descent for minimizing delay in the presence of stragglers")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "anytime stochastic gradient descent a time to hear from all the workers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive distributed stochastic gradient descent for minimizing delay in the presence of stragglers")
  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.IT(Information Theory)