human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification"):
  Abstract: Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series are a specific data type for which the traditional attribute-value representation, used predominantly in machine learning, fails to provide a relevant representation. HIVE-COTE combines multiple types of classifiers: each extracting information about a specific aspect of a time series, be it in the time domain, frequency domain or summarization of intervals within the series. However, HIVE-COTE (and its predecessor, FLAT-COTE) is often infeasible to run on even modest amounts of data. For instance, training HIVE-COTE on a dataset with only 1,500 time series can require 8 days of CPU time. It has polynomial runtime w.r.t training set size, so this problem compounds as data quantity increases. We propose a novel TSC algorithm, TS-CHIEF, which is highly competitive to HIVE-COTE in accuracy, but requires only a fraction of the runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time series that research has developed in the last decade. It uses tree-structured classifiers to do so efficiently. We assess TS-CHIEF on 85 datasets of the UCR archive, where it achieves state-of-the-art accuracy with scalability and efficiency. We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.

Includes members:
  Member "scalable dictionary classifiers for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tale of two toolkits report the first benchmarking time series classification algorithms for correctness and efficiency")
  Member "deep learning for time series classification a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier")
  Member "proximity forest an effective and scalable distance based classifier for time series":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for time series classification a review")
  Member "deep learning for sensor based activity recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty quantification for deep context aware mobile activity recognition and unknown context discovery")
  Member "temporal convolutional neural network for the classification of satellite image time series":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self attention for raw optical satellite time series classification")
  Member "from bop to boss and beyond time series classification with dictionary based classifiers":
    also connected through other hyperedges:
      (none)
  Member "scalable and accurate deep learning for electronic health records":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "the ucr time series archive":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering")

Question: Please tell me which class the source paper 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)