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: "automating biomedical data science through tree based pipeline optimization"
  Abstract: Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. 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 a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early 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 "automated machine learning in practice state of the art and recent results"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning neural models for end to end clustering"
    - "deep learning in the wild"
    - "large scale evolution of image classifiers"
    - "population based training of neural networks"
    - "neural networks for predicting algorithm runtime distributions"
    - "learning to learn by gradient descent by gradient descent"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-3 (induced by source paper "flo fast and lightweight hyperparameter optimization for automl"):
  with members:
    - "an open source automl benchmark"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"

Connected through hyperedge HE-4 (induced by source paper "darwinml a graph based evolutionary algorithm for automated machine learning"):
  with members:
    - "autostacker a compositional evolutionary learning system"
    - "progressive neural architecture search"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "large scale evolution of image classifiers"
    - "limits of end to end learning"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "towards modular and programmable architecture search"):
  with members:
    - "auto keras an efficient neural architecture search system"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "hierarchical representations for efficient architecture search"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "type driven automated learning with lale"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "xgboost a scalable tree boosting system"
    - "mllib machine learning in apache spark"
    - "deep residual learning for image recognition"
    - "api design for machine learning software experiences from the scikit learn project"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-7 (induced by source paper "transfer learning for risk classification of social media posts model evaluation study"):
  with members:
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "quantifying mental health from social media with neural user embeddings"
    - "empath understanding topic signals in large scale text"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "design of one year mortality forecast at hospital admission based a machine learning approach"):
  with members:
    - "improving palliative care with deep learning"
    - "tensorflow a system for large scale machine 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)