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: "hctsa a computational framework for automated time series phenotyping using massive feature extraction"
  Abstract: Summary  Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce  hctsa , a software tool for applying this methodological approach to data.  hctsa  includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how  hctsa  allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.

Connected through hyperedge HE-1 (induced by source paper "a physiology driven computational model for post cardiac arrest outcome prediction"):
  with members:
    - "deepsofa a continuous acuity score for critically ill patients using clinically interpretable deep learning"
    - "highly comparative feature based time series classification"

Connected through hyperedge HE-2 (induced by source paper "sktime a unified interface for machine learning with time series"):
  with members:
    - "machine learning automation toolbox mlaut"
    - "a tale of two toolkits report the first benchmarking time series classification algorithms for correctness and efficiency"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "gluonts probabilistic time series models in python"
    - "learning reductions that really work"
    - "a time series forest for classification and feature extraction"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-3 (induced by source paper "catch22 canonical time series characteristics"):
  with members:
    - "compengine a self organizing living library of time series data"
    - "the ucr time series archive"
    - "feature based time series analysis"
    - "highly comparative feature based time series classification"

Connected through hyperedge HE-4 (induced by source paper "compengine a self organizing living library of time series data"):
  with members:
    - "feature based time series analysis"
    - "highly comparative feature based time series classification"

Connected through hyperedge HE-5 (induced by source paper "feature engineering workflow for activity recognition from synchronized inertial measurement units"):
  with members:
    - "feature based time series analysis"
    - "distributed and parallel time series feature extraction for industrial big data applications"

Connected through hyperedge HE-6 (induced by source paper "feature based time series analysis"):
  with members:
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "highly comparative fetal heart rate analysis"

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)