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 "runtime optimization of join location in parallel data management systems"):
  Abstract: Applications running on parallel systems often need to join a streaming relation or a stored relation with data indexed in a parallel data storage system. Some applications also compute UDFs on the joined tuples. The join can be done at the data storage nodes, corresponding to reduce side joins, or by fetching data from the storage system to compute nodes, corresponding to map side join. Both may be suboptimal: reduce side joins may cause skew, while map side joins may lead to a lot of data being transferred and replicated. #R##N#In this paper, we present techniques to make runtime decisions between the two options on a per key basis, in order to improve the throughput of the join, accounting for UDF computation if any. Our techniques are based on an extended ski-rental algorithm and provide worst-case performance guarantees with respect to the optimal point in the space considered by us. Our techniques use load balancing taking into account the CPU, network and I/O costs as well as the load on compute and storage nodes. We have implemented our techniques on Hadoop, Spark and the Muppet stream processing engine. Our experiments show that our optimization techniques provide a significant improvement in throughput over existing techniques.

Includes members:
  Member "program transformations for asynchronous and batched query submission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cobra a framework for cost based rewriting of database applications")
  Member "automatic partitioning of database applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scheduling oltp transactions via machine learning")
  Member "cascading map side joins over hbase for scalable join processing":
    also connected through other hyperedges:
      (none)
  Member "muppet mapreduce style processing of fast data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comprehensive survey on parallelization and elasticity in stream processing")

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.DB(Databases)