human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "analysis of dawnbench a time to accuracy machine learning performance benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning")
  Member "lstm benchmarks for deep learning frameworks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tapirxla embedding fork join parallelism into the xla compiler in tensorflow using tapir")
  Member "latency and throughput characterization of convolutional neural networks for mobile computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis and characterization of training deep learning models on mobile devices")
  Member "characterizing co located datacenter workloads an alibaba case study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "across stack profiling and characterization of machine learning models on gpus")
  Member "tbd benchmarking and analyzing deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "tensorflow serving flexible high performance ml serving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "dlpaper2code auto generation of code from deep learning research papers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "modelhub ai dissemination platform for deep learning models")

Question: Please tell me which class the source paper 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)