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 "hierarchical coded matrix multiplication"):
  Abstract: In distributed computing systems slow working nodes, known as stragglers, can greatly extend finishing times. Coded computing is a technique that enables straggler-resistant computation. Most coded computing techniques presented to date provide robustness by ensuring that the time to finish depends only on a set of the fastest nodes. However, while stragglers do compute less work than non-stragglers, in real-world commercial cloud computing systems (e.g., Amazon's Elastic Compute Cloud (EC2)) the distinction is often a soft one. In this paper, we develop hierarchical coded computing that exploits the work completed by all nodes, both fast and slow, automatically integrating the potential contribution of each. We first present a conceptual framework to represent the division of work amongst nodes in coded matrix multiplication as a cuboid partitioning problem. This framework allows us to unify existing methods and motivates new techniques. We then develop three methods of hierarchical coded computing that we term bit-interleaved coded computation (BICC), multilevel coded computation (MLCC), and hybrid hierarchical coded computation (HHCC). In this paradigm, each worker is tasked with completing a sequence (a hierarchy) of ordered subtasks. The sequence of subtasks, and the complexity of each, is designed so that partial work completed by stragglers can be used in, rather than ignored. We note that our methods can be used in conjunction with any coded computing method. We illustrate this showing how we can use our methods to accelerate all previously developed coded computing technique by enabling them to exploit stragglers. Under a widely studied statistical model of completion time, our approach realizes a $66\%$ improvement in expected finishing time. On Amazon EC2, the gain was $28\%$ when stragglers are simulated.

Includes members:
  Member "computation scheduling for distributed machine learning with straggling workers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers")
  Member "cuboid partitioning for hierarchical coded matrix multiplication":
    also connected through other hyperedges:
      (none)
  Member "universally decodable matrices for distributed matrix vector multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "numerically stable binary gradient coding")
  Member "hierarchical 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 "c 3 les codes for coded computation that leverage stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely distributed computation with stragglers")
  Member "polynomially coded regression optimal straggler mitigation via data encoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coded federated learning")
  Member "a unified coded deep neural network training strategy based on generalized polydot codes":
    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")

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)