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: "selecting near optimal learners via incremental data allocation"
  Abstract: We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also minimizing the cost of misallocated samples. This is motivated by large modern datasets and ML toolkits with many combinations of learning algorithms and hyper-parameters. Inspired by the principle of "optimism under uncertainty," we propose an innovative strategy, Data Allocation using Upper Bounds (DAUB), which robustly achieves these objectives across a variety of real-world datasets. #R##N#We further develop substantial theoretical support for DAUB in an idealized setting where the expected accuracy of a classifier trained on $n$ samples can be known exactly. Under these conditions we establish a rigorous sub-linear bound on the regret of the approach (in terms of misallocated data), as well as a rigorous bound on suboptimality of the selected classifier. Our accuracy estimates using real-world datasets only entail mild violations of the theoretical scenario, suggesting that the practical behavior of DAUB is likely to approach the idealized behavior.

Connected through hyperedge HE-1 (induced by source paper "a system for massively parallel hyperparameter tuning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "a generalized framework for population based training"
    - "darts differentiable architecture search"
    - "tune a research platform for distributed model selection and training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "regularizing and optimizing lstm language models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-2 (induced by source paper "automated machine learning via admm"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "autostacker a compositional evolutionary learning system"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-3 (induced by source paper "an admm based framework for automl pipeline configuration"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "autostacker a compositional evolutionary learning system"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "exploiting reuse in pipeline aware hyperparameter tuning"):
  with members:
    - "layered tpot speeding up tree based pipeline optimization"
    - "population based training of neural networks"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "image based recommendations on styles and substitutes"
    - "mllib machine learning in apache spark"
    - "openml networked science in machine learning"

Connected through hyperedge HE-5 (induced by source paper "active learning for high dimensional binary features"):
  with members:
    - "learning active learning from data"
    - "can active learning experience be transferred"
    - "active learning with statistical models"

Connected through hyperedge HE-6 (induced by source paper "deep learning computational aspects"):
  with members:
    - "global explanations of neural networks mapping the landscape of predictions"
    - "tvm end to end optimization stack for deep learning"
    - "towards explainable deep learning for credit lending a case study"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "efficient natural language response suggestion for smart reply"
    - "attention based extraction of structured information from street view imagery"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "abc efficient selection of machine learning configuration on large dataset"):
  with members:
    - "hyperparameter optimization a spectral approach"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "openml networked science in machine learning"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-8 (induced by source paper "an empirical study on hyperparameter tuning of decision trees"):
  with members:
    - "bayesian hyperparameter optimization for ensemble learning"
    - "openml networked science in machine learning"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)