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: "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"
  Abstract: Hadoop is currently the large-scale data analysis "hammer" of choice, but there exist classes of algorithms that aren't "nails", in the sense that they are not particularly amenable to the MapReduce programming model. To address this, researchers have proposed MapReduce extensions or alternative programming models in which these algorithms can be elegantly expressed. This essay espouses a very different position: that MapReduce is "good enough", and that instead of trying to invent screwdrivers, we should simply get rid of everything that's not a nail. To be more specific, much discussion in the literature surrounds the fact that iterative algorithms are a poor fit for MapReduce: the simple solution is to find alternative non-iterative algorithms that solve the same problem. This essay captures my personal experiences as an academic researcher as well as a software engineer in a "real-world" production analytics environment. From this combined perspective I reflect on the current state and future of "big data" research.

Connected through hyperedge HE-1 (induced by source paper "smart data based ensemble for imbalanced big data classification"):
  with members:
    - "scarff a scalable framework for streaming credit card fraud detection with spark"
    - "catboost unbiased boosting with categorical features"
    - "enabling smart data noise filtering in big data classification"
    - "xgboost a scalable tree boosting system"
    - "mllib machine learning in apache spark"
    - "what will 5g be"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "enabling smart data noise filtering in big data classification"):
  with members:
    - "mllib machine learning in apache spark"
    - "towards ultrahigh dimensional feature selection for big data"
    - "identifying mislabeled training data"

Connected through hyperedge HE-3 (induced by source paper "greedy sparsity promoting algorithms for distributed learning"):
  with members:
    - "sparsity aware learning and compressed sensing an overview"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "distributed sparse signal recovery for sensor networks"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "the anatomy of big data computing"):
  with members:
    - "big data computing and clouds"
    - "vision paper towards an understanding of the limits of map reduce computation"
    - "crawling facebook for social network analysis purposes"

Connected through hyperedge HE-5 (induced by source paper "counting triangles in massive graphs with mapreduce"):
  with members:
    - "triadic measures on graphs the power of wedge sampling"
    - "counting triangles in real world graph streams dealing with repeated edges and time windows"
    - "parallel triangle counting in massive streaming graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "degree relations of triangles in real world networks and graph models"
    - "community structure and scale free collections of erdős renyi graphs"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-6 (induced by source paper "taming the zoo about algorithms implementation in the ecosystem of apache hadoop"):
  with members:
    - "how to perform research in hadoop environment not losing mental equilibrium case study"
    - "large scale citation matching using apache hadoop"

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.DC(Distributed, Parallel, and Cluster Computing)