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: "automl a survey of the state of the art"
  Abstract: Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline. Then we summarize existing Neural Architecture Search (NAS) research, which is one of the most popular topics in AutoML. We also compare the models generated by NAS algorithms with those human-designed models. Finally, we present several open problems for future research.

Connected through hyperedge HE-1 (induced by source paper "benchmark and survey of automated machine learning frameworks"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning in practice state of the art and recent results"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"

Connected through hyperedge HE-2 (induced by source paper "safe scalable automatic feature engineering framework for industrial tasks"):
  with members:
    - "interpretable mtl from heterogeneous domains using boosted tree"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "survey on automated machine learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "optimized cost per click in taobao display advertising"
    - "deep cross network for ad click predictions"
    - "one button machine for automating feature engineering in relational databases"

Connected through hyperedge HE-3 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "are all training examples created equal an empirical study"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-4 (induced by source paper "survey on automated machine learning"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning state of the art and open challenges"
    - "sparse recovery for orthogonal polynomial transforms"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "benchmarking automatic machine learning frameworks"

Connected through hyperedge HE-5 (induced by source paper "genetic neural architecture search for automatic assessment of human sperm images"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "auto keras an efficient neural architecture search system"
    - "path level network transformation for efficient architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models"):
  with members:
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "benchmarking the performance and power of ai accelerators for ai training"):
  with members:
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"
    - "mixed precision training"

Connected through hyperedge HE-8 (induced by source paper "automl using metadata language embeddings"):
  with members:
    - "an open source automl benchmark"
    - "fast task aware architecture inference"
    - "automatic machine learning by pipeline synthesis using model based reinforcement learning and a grammar"
    - "oboe collaborative filtering for automl model selection"
    - "survey on automated machine learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)