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 "a unified coded deep neural network training strategy based on generalized polydot codes"):
  Abstract: This paper has two main contributions. First, we propose a novel coding technique - Generalized PolyDot - for matrix-vector products that advances on existing techniques for coded matrix operations under storage and communication constraints. Next, we use Generalized PolyDot for the problem of training large Deep Neural Networks (DNNs) using unreliable nodes that are prone to soft-errors, e.g., bit flips during computation that produce erroneous outputs. An additional difficulty imposed by the problem of DNN training is that the parameter values (weight matrices) are updated at every iteration, and thus require a prohibitively large encoding cost at every iteration if we naively extend existing coded computing techniques. Thus, we propose a “unified” coded DNN training strategy where we weave coding into the operations of DNN training itself, so that the weight matrices, once initially encoded, remain encoded during updates with negligible encoding/decoding overhead per iteration. Moreover, our strategy can also allow for errors even in the nonlinear step of training. Finally, our coded DNN training strategy is completely decentralized: no assumptions on the presence of a master node are made, which avoids any single point of failure under soft-errors. Our strategy can provide unboundedly better error tolerance than the competing replication strategy and an MDS-code-based strategy [1].

Includes members:
  Member "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "hierarchical coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a locality based approach for coded computation")
  Member "learning a code machine learning for approximate non linear coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "straggler resilient and communication efficient distributed iterative linear solver":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding":
    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 "rateless codes for near perfect load balancing in distributed matrix vector multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "coded sparse matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")

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)