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 "performance evaluation of an algorithm based asynchronous checkpoint restart fault tolerant application using mixed mpi gpi 2"):
  Abstract: One of the hardest challenges of the current Big Data landscape is the lack of ability to process huge volumes of information in an acceptable time. The goal of this work, is to ascertain if it is useful to use typical Big Data tools to solve High Performance Computing problems, by exploring and comparing a distributed computing framework implemented on a commodity cluster architecture: the experiment will depend on the computational time required using tools such as Apache Spark. This will be compared to "equivalent more traditional" approaches such as using a distributed memory model with MPI on a distributed file system such as HDFS (Hadoop Distributed File System) and native C libraries that create an interface to encapsulate this file system functionalities, and using the GPI-2 implementation for the GASPI protocol and it's in-memory checkpointing library to provide an application with Fault Tolerance features. To be more precise, we've chosen the K-means algorithm as experiment, that will be ran on variable size datasets, and then we will compare the computational run time and time resilience of both approaches.

Includes members:
  Member "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "running alchemist on cray xc and cs series supercomputers dask and pyspark interfaces deployment options and data transfer times")
  Member "building a fault tolerant application using the gaspi communication layer":
    also connected through other hyperedges:
      (none)
  Member "a tale of two data intensive paradigms applications abstractions and architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "methods and experiences for developing abstractions for data intensive scientific applications")

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.DC(Distributed, Parallel, and Cluster Computing)