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: "automatic optimization for mapreduce programs"
  Abstract: The MapReduce distributed programming framework has become popular, despite evidence that current implementations are inefficient, requiring far more hardware than a traditional relational databases to complete similar tasks. MapReduce jobs are amenable to many traditional database query optimizations (B+Trees for selections, column-store- style techniques for projections, etc), but existing systems do not apply them, substantially because free-form user code obscures the true data operation being performed. For example, a selection in SQL is easily detected, but a selection in a MapReduce program is embedded in Java code along with lots of other program logic. We could ask the programmer to provide explicit hints about the program's data semantics, but one of MapReduce's attractions is precisely that it does not ask the user for such information. This paper covers Manimal, which automatically analyzes MapReduce programs and applies appropriate data- aware optimizations, thereby requiring no additional help at all from the programmer. We show that Manimal successfully detects optimization opportunities across a range of data operations, and that it yields speedups of up to 1,121% on previously-written MapReduce programs.

Connected through hyperedge HE-1 (induced by source paper "streaming space complexity of nearly all functions of one variable on frequency vectors"):
  with members:
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching and embedding are equivalent for norms"

Connected through hyperedge HE-2 (induced by source paper "equi depth histogram construction for big data with quality guarantees"):
  with members:
    - "only aggressive elephants are fast elephants"
    - "building wavelet histograms on large data in mapreduce"

Connected through hyperedge HE-3 (induced by source paper "the family of mapreduce and large scale data processing systems"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "restore reusing results of mapreduce jobs"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "stubby a transformation based optimizer for mapreduce workflows"
    - "fast clustering using mapreduce"
    - "load balancing for mapreduce based entity resolution"
    - "social content matching in mapreduce"
    - "column oriented storage techniques for mapreduce"

Connected through hyperedge HE-4 (induced by source paper "enabling operator reordering in data flow programs through static code analysis"):
  with members:
    - "opening the black boxes in data flow optimization"

Connected through hyperedge HE-5 (induced by source paper "only aggressive elephants are fast elephants"):
  with members:
    - "cophy a scalable portable and interactive index advisor for large workloads"
    - "column oriented storage techniques for mapreduce"

Connected through hyperedge HE-6 (induced by source paper "towards zero overhead adaptive indexing in hadoop"):
  with members:
    - "stochastic database cracking towards robust adaptive indexing in main memory column stores"
    - "only aggressive elephants are fast elephants"

Connected through hyperedge HE-7 (induced by source paper "perfxplain debugging mapreduce job performance"):
  with members:
    - "why did my query slow down"

Connected through hyperedge HE-8 (induced by source paper "opening the black boxes in data flow optimization"):
  with members:
    - "designing a multi petabyte database for lsst"

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)