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 "towards zero overhead adaptive indexing in hadoop"):
  Abstract: Several research works have focused on supporting index access in MapReduce systems. These works have allowed users to significantly speed up selective MapReduce jobs by orders of magnitude. However, all these proposals require users to create indexes upfront, which might be a difficult task in certain applications (such as in scientific and social applications) where workloads are evolving or hard to predict. To overcome this problem, we propose LIAH (Lazy Indexing and Adaptivity in Hadoop), a parallel, adaptive approach for indexing at minimal costs for MapReduce systems. The main idea of LIAH is to automatically and incrementally adapt to users' workloads by creating clustered indexes on HDFS data blocks as a byproduct of executing MapReduce jobs. Besides distributing indexing efforts over multiple computing nodes, LIAH also parallelises indexing with both map tasks computation and disk I/O. All this without any additional data copy in main memory and with minimal synchronisation. The beauty of LIAH is that it piggybacks index creation on map tasks, which read relevant data from disk to main memory anyways. Hence, LIAH does not introduce any additional read I/O-costs and exploit free CPU cycles. As a result and in contrast to existing adaptive indexing works, LIAH has a very low (or invisible) indexing overhead, usually for the very first job. Still, LIAH can quickly converge to a complete index, i.e. all HDFS data blocks are indexed. Especially, LIAH can trade early job runtime improvements with fast complete index convergence. We compare LIAH with HAIL, a state-of-the-art indexing technique, as well as with standard Hadoop with respect to indexing overhead and workload performance.

Includes members:
  Member "stochastic database cracking towards robust adaptive indexing in main memory column stores":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cracking in memory database index a case study for adaptive radix tree index")
  Member "only aggressive elephants are fast elephants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equi depth histogram construction for big data with quality guarantees")
  Member "automatic optimization for mapreduce programs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "streaming space complexity of nearly all functions of one variable on frequency vectors")

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)