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: "spinning fast iterative data flows"
  Abstract: Parallel dataflow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk iterative algorithms are supported by novel dataflow frameworks, these systems cannot exploit computational dependencies present in many algorithms, such as graph algorithms. As a result, these algorithms are inefficiently executed and have led to specialized systems based on other paradigms, such as message passing or shared memory. We propose a method to integrate incremental iterations, a form of workset iterations, with parallel dataflows. After showing how to integrate bulk iterations into a dataflow system and its optimizer, we present an extension to the programming model for incremental iterations. The extension alleviates for the lack of mutable state in dataflows and allows for exploiting the sparse computational dependencies inherent in many iterative algorithms. The evaluation of a prototypical implementation shows that those aspects lead to up to two orders of magnitude speedup in algorithm runtime, when exploited. In our experiments, the improved dataflow system is highly competitive with specialized systems while maintaining a transparent and unified dataflow abstraction.

Connected through hyperedge HE-1 (induced by source paper "high level programming abstractions for distributed graph processing"):
  with members:
    - "the gremlin graph traversal machine and language invited talk"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "pregelix big ger graph analytics on a dataflow engine"

Connected through hyperedge HE-2 (induced by source paper "a survey of state management in big data processing systems"):
  with members:
    - "elasticutor rapid elasticity for realtime stateful stream processing"
    - "incremental query processing on big data streams"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "frogwild fast pagerank approximations on graph engines"
    - "incremental computation with names"
    - "s store streaming meets transaction processing"
    - "tolerating correlated failures in massively parallel stream processing engines"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-3 (induced by source paper "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"):
  with members:
    - "using a power law distribution to describe big data"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "pagerank beyond the web"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-4 (induced by source paper "declarative machine learning a classification of basic properties and types"):
  with members:
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-5 (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"
    - "can the elephants handle the nosql onslaught"

Connected through hyperedge HE-6 (induced by source paper "efficient iterative processing in the scidb parallel array engine"):
  with members:
    - "rex recursive delta based data centric computation"
    - "riot i o efficient numerical computing without sql"

Connected through hyperedge HE-7 (induced by source paper "pregelix big ger graph analytics on a dataflow engine"):
  with members:
    - "scaling datalog for machine learning on big data"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "rex recursive delta based data centric computation"

Connected through hyperedge HE-8 (induced by source paper "graphx unifying data parallel and graph parallel analytics"):
  with members:
    - "shark sql and rich analytics at scale"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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)