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 "mds coding is better than replication for job completion times"):
  Abstract: In a multi-server system, how can one get better performance than random assignment of jobs to servers if queue-states cannot be queried by the dispatcher? A replication strategy has recently been proposed where $d$ copies of each arriving job are sent to servers chosen at random. The job's completion time is the first time that the service of any of its copies is complete. On completion, redundant copies of the job are removed from other queues so as not to overburden the system. #R##N#For digital jobs, where the objects to be served can be algebraically manipulated, and for servers whose ouput is a linear function of their input, here we consider an alternate strategy: Maximum Distance Separable (MDS) codes. For every batch of $n$ digital jobs that arrive, $n+m$ linear combinations are created over the reals or a large finite field, and each coded job is sent to a random server. The batch completion time is the first time that any $n$ of the $n+m$ coded jobs are served, as the evaluation of $n$ original jobs can be recovered by Gaussian elimination. If redundant jobs can be removed from queues on batch completion, we establish that in order to get the improved response-time performance of sending $d$ copies of each of $n$ jobs via the replication strategy, with the MDS methodology it suffices to send $n+d$ jobs. That is, while replication is multiplicative, MDS is linear. If redunant jobs cannot be removed from queues on batch completion, the stability regions of the two strategies are distinct and the performance with MDS codes is better still.

Includes members:
  Member "gradient coding with clustering and multi message communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "numerically stable binary gradient coding")
  Member "speeding up distributed machine learning using codes":
    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 "speeding up distributed gradient descent by utilizing non persistent stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "timely distributed computation with stragglers")
  Member "improving distributed gradient descent using reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a locality based approach for coded computation")
  Member "when do redundant requests reduce latency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  Member "on the delay storage trade off in content download from coded distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes")
  Member "the mds queue analysing the latency performance of erasure codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes")
  Member "network coding meets tcp":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimum latency fec design with delayed feedback mathematical modeling and efficient algorithms")

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.PF(Performance)