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: "the family of mapreduce and large scale data processing systems"
  Abstract: In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.

Connected through hyperedge HE-1 (induced by source paper "a survey on big data for network traffic monitoring and analysis"):
  with members:
    - "data analytics service composition and deployment on edge devices"
    - "net2vec deep learning for the network"
    - "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a new approach to dimensionality reduction for anomaly detection in data traffic"
    - "efficacy of live ddos detection with hadoop"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-2 (induced by source paper "a taxonomy on big data survey"):
  with members:
    - "mapreduce scheduler a 360 degree view"
    - "internet of things iot a vision architectural elements and future directions"
    - "restore reusing results of mapreduce jobs"
    - "early accurate results for advanced analytics on mapreduce"

Connected through hyperedge HE-3 (induced by source paper "taxonomy of big data a survey"):
  with members:
    - "mapreduce scheduler a 360 degree view"
    - "mllib machine learning in apache spark"
    - "internet of things iot a vision architectural elements and future directions"
    - "early accurate results for advanced analytics on mapreduce"

Connected through hyperedge HE-4 (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-5 (induced by source paper "security and privacy aspects in mapreduce on clouds a survey"):
  with members:
    - "assignment problems of different sized inputs in mapreduce"
    - "computing marginals using mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"
    - "on the computational complexity of mapreduce"
    - "enumerating subgraph instances using map reduce"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "efficient multi way theta join processing using mapreduce"
    - "vision paper towards an understanding of the limits of map reduce computation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "towards ultrahigh dimensional feature selection for big data"

Connected through hyperedge HE-7 (induced by source paper "sharesskew an algorithm to handle skew for joins in mapreduce"):
  with members:
    - "worst case optimal algorithms for parallel query processing"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "α k minimal sorting and skew join in mpi and mapreduce"
    - "skew in parallel query processing"
    - "gym a multiround join algorithm in mapreduce"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"
    - "communication steps for parallel query processing"

Connected through hyperedge HE-8 (induced by source paper "on the inequality of the 3v s of big data architectural paradigms a case for heterogeneity"):
  with members:
    - "shark sql and rich analytics at scale"
    - "a taxonomy of data grids for distributed data sharing management and processing"

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)