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 "speeding up distributed machine learning using codes"):
  Abstract: Codes are widely used in many engineering applications to offer  robustness  against  noise . In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms—straggler nodes, system failures, or communication bottlenecks—but there has been little interaction cutting across codes, machine learning, and distributed systems. In this paper, we provide theoretical insights on how  coded  solutions can achieve significant gains compared with uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms:  matrix multiplication  and  data shuffling . For matrix multiplication, we use codes to alleviate the effect of stragglers and show that if the number of homogeneous workers is    $n$   , and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of    $\log n$   . For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction    $\alpha $    of the data matrix can be cached at each worker, and    $n$    is the number of workers,  coded shuffling  reduces the communication cost by a factor of    $\left({\alpha + \frac {1}{n}}\right)\gamma (n)$    compared with uncoded shuffling, where    $\gamma (n)$    is the ratio of the cost of unicasting    $n$    messages to    $n$    users to multicasting a common message (of the same size) to    $n$    users. For instance,    $\gamma (n) \simeq n$    if multicasting a message to    $n$    users is as cheap as unicasting a message to one user. We also provide experimental results, corroborating our theoretical gains of the coded algorithms.

Includes members:
  Member "a pliable index coding approach to data shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "coded computation over heterogeneous clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "short dot computing large linear transforms distributedly using coded short dot products":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "minimizing latency for secure distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive distributed stochastic gradient descent for minimizing delay in the presence of stragglers")
  Member "a fundamental tradeoff between computation and communication in distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "a unified coding framework for distributed computing with straggling servers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a locality based approach for coded computation")
  Member "on the worst case communication overhead for distributed data shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless mapreduce distributed computing")
  Member "information theoretic limits of data shuffling for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "coded distributed computing over packet erasure channels")

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)