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: "restore reusing results of mapreduce jobs"
  Abstract: Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce programming and execution model and its implementations, most notably Hadoop. Users of MapReduce often have analysis tasks that are too complex to express as individual MapReduce jobs. Instead, they use high-level query languages such as Pig, Hive, or Jaql to express their complex tasks. The compilers of these languages translate queries into workflows of MapReduce jobs. Each job in these workflows reads its input from the distributed file system used by the MapReduce system and produces output that is stored in this distributed file system and read as input by the next job in the workflow. The current practice is to delete these intermediate results from the distributed file system at the end of executing the workflow. One way to improve the performance of workflows of MapReduce jobs is to keep these intermediate results and reuse them for future workflows submitted to the system. In this paper, we present ReStore, a system that manages the storage and reuse of such intermediate results. ReStore can reuse the output of whole MapReduce jobs that are part of a workflow, and it can also create additional reuse opportunities by materializing and storing the output of query execution operators that are executed within a MapReduce job. We have implemented ReStore as an extension to the Pig dataflow system on top of Hadoop, and we experimentally demonstrate significant speedups on queries from the PigMix benchmark.

Connected through hyperedge HE-1 (induced by source paper "a taxonomy on big data survey"):
  with members:
    - "mapreduce scheduler a 360 degree view"
    - "the family of mapreduce and large scale data processing systems"
    - "internet of things iot a vision architectural elements and future directions"
    - "early accurate results for advanced analytics on mapreduce"

Connected through hyperedge HE-2 (induced by source paper "helix holistic optimization for accelerating iterative machine learning"):
  with members:
    - "accelerating human in the loop machine learning challenges and opportunities"
    - "helix accelerating human in the loop machine learning"
    - "how developers iterate on machine learning workflows a survey of the applied machine learning literature"
    - "snorkel rapid training data creation with weak supervision"
    - "towards linear algebra over normalized data"
    - "activeclean interactive data cleaning while learning convex loss models"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "declarative machine learning a classification of basic properties and types"

Connected through hyperedge HE-3 (induced by source paper "cache based multi query optimization for data intensive scalable computing frameworks"):
  with members:
    - "revisiting reuse in main memory database systems"
    - "shareddb killing one thousand queries with one stone"
    - "efficient and extensible algorithms for multi query optimization"

Connected through hyperedge HE-4 (induced by source paper "keystoneml optimizing pipelines for large scale advanced analytics"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "youtube 8m a large scale video classification benchmark"
    - "caffe con troll shallow ideas to speed up deep learning"
    - "inferring networks of substitutable and complementary products"
    - "mllib machine learning in apache spark"
    - "rethinking the inception architecture for computer vision"
    - "dimmwitted a study of main memory statistical analytics"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-5 (induced by source paper "robus fair cache allocation for multi tenant data parallel workloads"):
  with members:
    - "competitive algorithms from competitive equilibria non clairvoyant scheduling under polyhedral constraints"
    - "optimal auctions via the multiplicative weight method"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-6 (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"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "stubby a transformation based optimizer for mapreduce workflows"
    - "automatic optimization for mapreduce programs"
    - "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-7 (induced by source paper "exploiting opportunistic physical design in large scale data analytics"):
  with members:
    - "towards a workload for evolutionary analytics"
    - "opening the black boxes in data flow optimization"

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)