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: "edge aibench towards comprehensive end to end edge computing benchmarking"
  Abstract: In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil's coordinated e ort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is a part of the open-source AIBench project, publicly available from this http URL. We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues.

Connected through hyperedge HE-1 (induced by source paper "ai oriented medical workload allocation for hierarchical cloud edge device computing"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "ai benchmark running deep neural networks on android smartphones"
    - "pruning convolutional neural networks for resource efficient inference"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "aibench an industry standard internet service ai benchmark suite"):
  with members:
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "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"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"

Connected through hyperedge HE-4 (induced by source paper "benchcouncil s view on benchmarking ai and other emerging workloads"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "mlperf inference benchmark"
    - "mlperf training benchmark"
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "benchmark analysis of representative deep neural network architectures"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"

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)