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 "leveraging coding techniques for speeding up distributed computing"):
  Abstract: Large scale clusters leveraging distributed computing frameworks such as MapReduce routinely process data that are on the orders of petabytes or more. The sheer size of the data precludes the processing of the data on a single computer. The philosophy in these methods is to partition the overall job into smaller tasks that are executed on different servers; this is called the map phase. This is followed by a data shuffling phase where appropriate data is exchanged between the servers. The final so-called reduce phase, completes the computation. #R##N#One potential approach, explored in prior work for reducing the overall execution time is to operate on a natural tradeoff between computation and communication. Specifically, the idea is to run redundant copies of map tasks that are placed on judiciously chosen servers. The shuffle phase exploits the location of the nodes and utilizes coded transmission. The main drawback of this approach is that it requires the original job to be split into a number of map tasks that grows exponentially in the system parameters. This is problematic, as we demonstrate that splitting jobs too finely can in fact adversely affect the overall execution time. #R##N#In this work we show that one can simultaneously obtain low communication loads while ensuring that jobs do not need to be split too finely. Our approach uncovers a deep relationship between this problem and a class of combinatorial structures called resolvable designs. Appropriate interpretation of resolvable designs can allow for the development of coded distributed computing schemes where the splitting levels are exponentially lower than prior work. We present experimental results obtained on Amazon EC2 clusters for a widely known distributed algorithm, namely TeraSort. We obtain over 4.69$\times$ improvement in speedup over the baseline approach and more than 2.6$\times$ over current state of the art.

Includes members:
  Member "low subpacketization schemes for coded caching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamental limits of caching in heterogeneous networks with uncoded prefetching")
  Member "coded caching schemes with reduced subpacketization from linear block codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "a fundamental tradeoff between computation and communication in distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "coded caching for networks with the resolvability property":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "sum networks from incidence structures construction and capacity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zero error function computation on a directed acyclic network")
  Member "fractional repetition codes with flexible repair from combinatorial designs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal fraction repetition codes for access balancing in distributed storage")
  Member "fundamental limits of caching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")

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)