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: "characterizing data analysis workloads in data centers"
  Abstract: As the amount of data explodes rapidly, more and more corporations are using data centers to make effective decisions and gain a competitive edge. Data analysis applications play a significant role in data centers, and hence it has became increasingly important to understand their behaviors in order to further improve the performance of data center computer systems. In this paper, after investigating three most important application domains in terms of page views and daily visitors, we choose eleven representative data analysis workloads and characterize their micro-architectural characteristics by using hardware performance counters, in order to understand the impacts and implications of data analysis workloads on the systems equipped with modern superscalar out-of-order processors. Our study on the workloads reveals that data analysis applications share many inherent characteristics, which place them in a different class from desktop (SPEC CPU2006), HPC (HPCC), and service workloads, including traditional server workloads (SPECweb2005) and scale-out service workloads (four among six benchmarks in CloudSuite), and accordingly we give several recommendations for architecture and system optimizations. On the basis of our workload characterization work, we released a benchmark suite named DCBench for typical datacenter workloads, including data analysis and service workloads, with an open-source license on our project home page on this http URL We hope that DCBench is helpful for performing architecture and small-to-medium scale system researches for datacenter computing.

Connected through hyperedge HE-1 (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-2 (induced by source paper "hpc ai500 a benchmark suite for hpc ai systems"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "tbd benchmarking and analyzing deep neural network training"
    - "globenet convolutional neural networks for typhoon eye tracking from remote sensing imagery"
    - "scaling grpc tensorflow on 512 nodes of cori supercomputer"
    - "benchmarking state of the art deep learning software tools"

Connected through hyperedge HE-3 (induced by source paper "defog fog computing benchmarks"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "yolov3 an incremental improvement"
    - "edgebench benchmarking edge computing platforms"
    - "enorm a framework for edge node resource management"
    - "doclite a docker based lightweight cloud benchmarking tool"
    - "challenges and opportunities in edge computing"
    - "container based cloud virtual machine benchmarking"

Connected through hyperedge HE-4 (induced by source paper "edge aibench towards comprehensive end to end edge computing benchmarking"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "federated machine learning concept and applications"
    - "ai benchmark running deep neural networks on android smartphones"
    - "edgebench benchmarking edge computing platforms"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "xos an application defined operating system for data center servers"):
  with members:
    - "on horizontal decomposition of the operating system"
    - "isolate first then share a new os architecture for datacenter computing"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-6 (induced by source paper "storage and memory characterization of data intensive workloads for bare metal cloud"):
  with members:
    - "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"
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"

Connected through hyperedge HE-8 (induced by source paper "understanding big data analytic workloads on modern processors"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "bigop generating comprehensive big data workloads as a benchmarking framework"
    - "characterizing and subsetting big data workloads"
    - "bigdatabench a big data benchmark suite from web search engines"
    - "high volume throughput computing identifying and characterizing throughput oriented 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.PF(Performance)