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: "frustrated with replicating claims of a shared model a solution"
  Abstract: Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them. This is exacerbated by the complicated procedures for evaluation. The lack of standard systems and efficient techniques for specifying and provisioning ML/DL evaluation is the main cause of this "pain point". This work discusses common pitfalls for replicating DL model evaluation, and shows that these subtle pitfalls can affect both accuracy and performance. It then proposes a solution to remedy these pitfalls called MLModelScope, a specification for repeatable model evaluation and a runtime to provision and measure experiments. We show that by easing the model specification and evaluation process, MLModelScope facilitates rapid adoption of ML/DL innovations.

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"
    - "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-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"
    - "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 "nais neural architecture and implementation search and its applications in autonomous driving"):
  with members:
    - "skynet a champion model for dac sdc on low power object detection"
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "yolov3 an incremental improvement"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "hardware aware machine learning modeling and optimization"
    - "searching toward pareto optimal device aware neural architectures"
    - "learning transferable architectures for scalable image recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "across stack profiling and characterization of machine learning models on gpus"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "performance modelling of deep learning on intel many integrated core architectures"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "lstm benchmarks for deep learning frameworks"
    - "characterizing co located datacenter workloads an alibaba case study"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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.LG(Machine Learning)