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: "distributed and parallel time series feature extraction for industrial big data applications"
  Abstract: The all-relevant problem of feature selection is the identification of all strongly and weakly relevant attributes. This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously. Here, we are proposing an efficient, scalable feature extraction algorithm for time series, which filters the available features in an early stage of the machine learning pipeline with respect to their significance for the classification or regression task, while controlling the expected percentage of selected but irrelevant features. The proposed algorithm combines established feature extraction methods with a feature importance filter. It has a low computational complexity, allows to start on a problem with only limited domain knowledge available, can be trivially parallelized, is highly scalable and based on well studied non-parametric hypothesis tests. We benchmark our proposed algorithm on all binary classification problems of the UCR time series classification archive as well as time series from a production line optimization project and simulated stochastic processes with underlying qualitative change of dynamics.

Connected through hyperedge HE-1 (induced by source paper "probabilistic modeling for novelty detection with applications to fraud identification"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "a comparative evaluation of novelty detection algorithms for discrete sequences"
    - "visual interpretability for deep learning a survey"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "variational auto encoded deep gaussian processes"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "wearable based parkinson s disease severity monitoring using deep learning"):
  with members:
    - "deep learning for time series classification a review"
    - "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
    - "wearable based mediation state detection in individuals with parkinson s disease"
    - "transfer learning for time series classification"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "the autofeat python library for automatic feature engineering and selection"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "one button machine for automating feature engineering in relational databases"
    - "extrapolation and learning equations"
    - "why should i trust you explaining the predictions of any classifier"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deep learning of representations looking forward"

Connected through hyperedge HE-4 (induced by source paper "a comparative evaluation of novelty detection algorithms for discrete sequences"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "visual interpretability for deep learning a survey"
    - "a critical review of recurrent neural networks for sequence learning"
    - "a meta analysis of the anomaly detection problem"
    - "effective approaches to attention based neural machine translation"
    - "visualizing and understanding recurrent networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "activity archetypes in question and answer q a websites a study of 50 stack exchange instances"):
  with members:
    - "activity dynamics in collaboration networks"
    - "chaff from the wheat characterization and modeling of deleted questions on stack overflow"
    - "social status and badge design"

Connected through hyperedge HE-6 (induced by source paper "feature engineering workflow for activity recognition from synchronized inertial measurement units"):
  with members:
    - "feature based time series analysis"
    - "hctsa a computational framework for automated time series phenotyping using massive feature extraction"

Connected through hyperedge HE-7 (induced by source paper "towards a near universal time series data mining tool introducing the matrix profile"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "asap prioritizing attention via time series smoothing"
    - "m tsne a framework for visualizing high dimensional multivariate time series"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "node2vec scalable feature learning for networks"
    - "adversarial feature learning"

Connected through hyperedge HE-8 (induced by source paper "not to cry wolf distantly supervised multitask learning in critical care"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "good semi supervised learning that requires a bad gan"
    - "triple generative adversarial nets"
    - "multi task self supervised visual 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)