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: "evaluation of a tree based pipeline optimization tool for automating data science"
  Abstract: As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

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"
    - "automl a survey of the state of the art"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "lifting interpretability performance trade off via automated feature engineering"):
  with members:
    - "the autofeat python library for automatic feature engineering and selection"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "magix model agnostic globally interpretable explanations"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "xgboost a scalable tree boosting system"
    - "openml networked science in machine learning"

Connected through hyperedge HE-3 (induced by source paper "an adaptive and near parameter free evolutionary computation approach towards true automation in automl"):
  with members:
    - "an open source automl benchmark"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "a large population size can be unhelpful in evolutionary algorithms"

Connected through hyperedge HE-4 (induced by source paper "improving generalisation of automl systems with dynamic fitness evaluations"):
  with members:
    - "an open source automl benchmark"
    - "towards automated machine learning evaluation and comparison of automl approaches and tools"
    - "regularized evolution for image classifier architecture search"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-5 (induced by source paper "scalable reinforcement learning based neural architecture search for cancer deep learning research"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "evaluating the search phase of neural architecture search"
    - "balsam automated scheduling and execution of dynamic data intensive hpc workflows"
    - "evolutionary neural architecture search for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"

Connected through hyperedge HE-6 (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"
    - "automl a survey of the state of the art"
    - "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-7 (induced by source paper "techniques for automated machine learning"):
  with members:
    - "automating predictive modeling process using reinforcement learning"
    - "adanet a scalable and flexible framework for automatically learning ensembles"
    - "nas bench 101 towards reproducible neural architecture search"
    - "darwinml a graph based evolutionary algorithm for automated machine learning"
    - "autostacker a compositional evolutionary learning system"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "taking human out of learning applications a survey on automated machine learning"
    - "neural architecture optimization"

Connected through hyperedge HE-8 (induced by source paper "the machine learning bazaar harnessing the ml ecosystem for effective system development"):
  with members:
    - "neuronblocks building your nlp dnn models like playing lego"
    - "massively parallel hyperparameter tuning"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "metadata embeddings for user and item cold start recommendations"

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.NE(Neural and Evolutionary Computing)