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: "bdgs a scalable big data generator suite in big data benchmarking"
  Abstract: Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data).

Connected through hyperedge HE-1 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite"):
  with members:
    - "mlperf training benchmark"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "aibench an industry standard internet service ai benchmark suite"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "an on the fly framework for efficiently generating synthetic big data sets"):
  with members:
    - "generation of synthetic electronic medical record text"
    - "virtual to real reinforcement learning for autonomous driving"
    - "deep learning in neural networks"
    - "action conditional video prediction using deep networks in atari games"

Connected through hyperedge HE-4 (induced by source paper "data motif based proxy benchmarks for big data and ai workloads"):
  with members:
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "fathom reference workloads for modern deep learning methods"
    - "rethinking the inception architecture for computer vision"
    - "the gap benchmark suite"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"

Connected through hyperedge HE-5 (induced by source paper "a dwarf based scalable big data benchmarking methodology"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"

Connected through hyperedge HE-6 (induced by source paper "plug and play bench simplifying big data benchmarking using containers"):
  with members:
    - "identifying dwarfs workloads in big data analytics"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-7 (induced by source paper "architectural impact on performance of in memory data analytics apache spark case study"):
  with members:
    - "mllib machine learning in apache spark"
    - "performance characterization of in memory data analytics on a modern cloud server"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "characterizing data analysis workloads in data centers"

Connected through hyperedge HE-8 (induced by source paper "how data volume affects spark based data analytics on a scale up server"):
  with members:
    - "performance characterization of in memory data analytics on a modern cloud server"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "characterizing data analysis workloads in data centers"

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)