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 "weighted gradient coding with leverage score sampling"):
  Abstract: A major hurdle in machine learning is scalability to massive datasets. Approaches to overcome this hurdle include compression of the data matrix and distributing the computations. \textit{Leverage score sampling} provides a compressed approximation of a data matrix using an importance weighted subset. \textit{Gradient coding} has been recently proposed in distributed optimization to compute the gradient using multiple unreliable worker nodes. By designing coding matrices, gradient coded computations can be made resilient to stragglers, which are nodes in a distributed network that degrade system performance. We present a novel \textit{weighted leverage score} approach, that achieves improved performance for distributed gradient coding by utilizing an importance sampling.

Includes members:
  Member "numerically stable binary gradient coding":
    also connected through other hyperedges:
      (none)
  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 "gradient coding based on block designs for mitigating adversarial stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "numerically stable binary gradient coding")
  Member "gradient coding with clustering and multi message communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "numerically stable binary gradient coding")
  Member "distributed stochastic gradient descent using ldgm codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "a locality based approach for coded computation")
  Member "gradient coding from cyclic mds codes and expander graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a locality based approach for coded computation")
  Member "balanced reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gabidulin codes with support constrained generator matrices")

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)