human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "can the elephants handle the nosql onslaught"
  Abstract: In this new era of "big data", traditional DBMSs are under attack from two sides. At one end of the spectrum, the use of document store NoSQL systems (e.g. MongoDB) threatens to move modern Web 2.0 applications away from traditional RDBMSs. At the other end of the spectrum, big data DSS analytics that used to be the domain of parallel RDBMSs is now under attack by another class of NoSQL data analytics systems, such as Hive on Hadoop. So, are the traditional RDBMSs, aka "big elephants", doomed as they are challenged from both ends of this "big data" spectrum? In this paper, we compare one representative NoSQL system from each end of this spectrum with SQL Server, and analyze the performance and scalability aspects of each of these approaches (NoSQL vs. SQL) on two workloads (decision support analysis and interactive data-serving) that represent the two ends of the application spectrum. We present insights from this evaluation and speculate on potential trends for the future.

Connected through hyperedge HE-1 (induced by source paper "a comparative analysis of state of the art sql on hadoop systems for interactive analytics"):
  with members:
    - "asterixdb a scalable open source bdms"
    - "shark sql and rich analytics at scale"

Connected through hyperedge HE-2 (induced by source paper "transactional partitioning a new abstraction for main memory databases"):
  with members:
    - "warp lightweight multi key transactions for key value stores"

Connected through hyperedge HE-3 (induced by source paper "empirical big data research a systematic literature mapping"):
  with members:
    - "performance evaluation of a natural language processing approach applied in white collar crime investigation"
    - "performance benefits of datampi a case study with bigdatabench"
    - "multilevel hierarchical kernel spectral clustering for real life large scale complex networks"
    - "graphical evolutionary game for information diffusion over social networks"
    - "the family of mapreduce and large scale data processing systems"
    - "optimizing i o for big array analytics"
    - "pf ola a high performance framework for parallel on line aggregation"
    - "towards ultrahigh dimensional feature selection for big data"

Question: Please tell me which class the center node 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)