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: "opening the black boxes in data flow optimization"
  Abstract: Many systems for big data analytics employ a data flow abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally, query optimization is applied to queries with known algebraic semantics. In this work, we find that a handful of properties, rather than a full algebraic specification, suffice to establish reordering conditions for data processing operators. We show that these properties can be accurately estimated for black box operators by statically analyzing the general-purpose code of their user-defined functions. We design and implement an optimizer for parallel data flows that does not assume knowledge of semantics or algebraic properties of operators. Our evaluation confirms that the optimizer can apply common rewritings such as selection reordering, bushy join-order enumeration, and limited forms of aggregation push-down, hence yielding similar rewriting power as modern relational DBMS optimizers. Moreover, it can optimize the operator order of non-relational data flows, a unique feature among today's systems.

Connected through hyperedge HE-1 (induced by source paper "the nebulastream platform data and application management for the internet of things"):
  with members:
    - "benchmarking distributed stream data processing systems"
    - "streambox tz a secure iot analytics engine at the edge"
    - "iot stream processing and analytics in the fog"

Connected through hyperedge HE-2 (induced by source paper "query and resource optimizations a case for breaking the wall in big data systems"):
  with members:
    - "a fast randomized algorithm for multi objective query optimization"
    - "stubby a transformation based optimizer for mapreduce workflows"

Connected through hyperedge HE-3 (induced by source paper "the many faces of data centric workflow optimization a survey"):
  with members:
    - "cost optimization of data flows based on task re ordering"
    - "monetary cost optimizations for hosting workflow as a service in iaas clouds"
    - "stubby a transformation based optimizer for mapreduce workflows"

Connected through hyperedge HE-4 (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-5 (induced by source paper "exploiting opportunistic physical design in large scale data analytics"):
  with members:
    - "towards a workload for evolutionary analytics"
    - "restore reusing results of mapreduce jobs"

Connected through hyperedge HE-6 (induced by source paper "enabling operator reordering in data flow programs through static code analysis"):
  with members:
    - "automatic optimization for mapreduce programs"

Connected through hyperedge HE-7 (induced by source paper "spinning fast iterative data flows"):
  with members:
    - "scaling datalog for machine learning on big data"
    - "graphlab a new framework for parallel machine learning"

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)