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: "infrastructure for usable machine learning the stanford dawn project"
  Abstract: Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.

Connected through hyperedge HE-1 (induced by source paper "democratisation of usable machine learning in computer vision"):
  with members:
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "panda facilitating usable ai development"):
  with members:
    - "rafiki machine learning as an analytics service system"
    - "medical concept embedding with time aware attention"
    - "deep forest"
    - "forkbase an efficient storage engine for blockchain and forkable applications"
    - "progressive neural architecture search"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "the case for learned index structures"
    - "thoracic disease identification and localization with limited supervision"

Connected through hyperedge HE-3 (induced by source paper "big data systems meet machine learning challenges towards big data science as a service"):
  with members:
    - "f2 reenvisioning data analytics systems"
    - "modelhub towards unified data and lifecycle management for deep learning"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "declarative machine learning a classification of basic properties and types"
    - "mllib machine learning in apache spark"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "the madlib analytics library or mad skills the sql"
    - "distributed graphlab a framework for machine learning in the cloud"

Connected through hyperedge HE-4 (induced by source paper "ease ml towards multi tenant resource sharing for machine learning workloads"):
  with members:
    - "mlbench how good are machine learning clouds for binary classification tasks on structured data"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "the madlib analytics library or mad skills the sql"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)