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 "benchmarking top k keyword and top k document processing with t2k2 and t2k2d2"):
  Abstract: Top-  k     k        keyword and top-  k     k        document extraction are very popular text analysis techniques. Top-  k     k        keywords and documents are often computed on-the-fly, but they exploit weighted vocabularies that are costly to build. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present T   2          2         K   2          2         , a top-  k     k        keywords and documents benchmark, and its decision support-oriented evolution T   2          2         K   2          2         D   2          2         . Both benchmarks feature a real tweet dataset and queries with various complexities and selectivities. They help evaluate weighting schemes and database implementations in terms of computing performance. To illustrate our benchmarks’ relevance and genericity, we successfully ran performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand.

Includes members:
  Member "a scalable document based architecture for text analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "t 2 k 2 the twitter top k keywords benchmark")
  Member "event detection tracking and visualization in twitter a mention anomaly based approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conversational structure aware and context sensitive topic model for online discussions")
  Member "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "bigdatabench a big data benchmark suite from internet services":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tlb and pagewalk performance in multicore architectures with large die stacked dram cache")
  Member "primeball a parallel processing framework benchmark for big data applications in the cloud":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "t 2 k 2 the twitter top k keywords benchmark")

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)