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: "an empirical evaluation of similarity measures for time series classification"
  Abstract: Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similarity have been proposed. However, there is a lack of comparative studies using empirical, rigorous, quantitative, and large-scale assessment strategies. In this article, we provide an extensive evaluation of similarity measures for time series classification following the aforementioned principles. We consider 7 different measures coming from alternative measure 'families', and 45 publicly-available time series data sets coming from a wide variety of scientific domains. We focus on out-of-sample classification accuracy, but in-sample accuracies and parameter choices are also discussed. Our work is based on rigorous evaluation methodologies and includes the use of powerful statistical significance tests to derive meaningful conclusions. The obtained results show the equivalence, in terms of accuracy, of a number of measures, but with one single candidate outperforming the rest. Such findings, together with the followed methodology, invite researchers on the field to adopt a more consistent evaluation criteria and a more informed decision regarding the baseline measures to which new developments should be compared.

Connected through hyperedge HE-1 (induced by source paper "an overview of distance and similarity functions for structured data"):
  with members:
    - "graph matching networks for learning the similarity of graph structured objects"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "a survey on metric learning for feature vectors and structured data"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "support vector machine classification with indefinite kernels"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

Connected through hyperedge HE-2 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries"):
  with members:
    - "robust densest subgraph discovery"
    - "on finding dense common subgraphs"
    - "twawler a lightweight twitter crawler"
    - "risk averse matchings over uncertain graph databases"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "robust influence maximization"
    - "robust influence maximization"
    - "best friends forever bff finding lasting dense subgraphs"

Connected through hyperedge HE-3 (induced by source paper "dynamic time warp convolutional networks"):
  with members:
    - "deep learning for time series classification a review"
    - "neuralwarp time series similarity with warping networks"
    - "deep learning for time series analysis"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "application of machine learning to accidents detection at directional drilling"):
  with members:
    - "real time data driven detection of the rock type alteration during a directional drilling"
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "data driven model for the identification of the rock type at a drilling bit"
    - "an experimental evaluation of nearest neighbour time series classification"

Connected through hyperedge HE-5 (induced by source paper "clustering time series by a novel slope based similarity measure considering particle swarm optimization"):
  with members:
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "clustering time series and the surprising robustness of hmms"
    - "clustering time series data stream a literature survey"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-6 (induced by source paper "failures detection at directional drilling using real time analogues search"):
  with members:
    - "real time data driven detection of the rock type alteration during a directional drilling"
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "data driven model for the identification of the rock type at a drilling bit"
    - "an experimental evaluation of nearest neighbour time series classification"

Connected through hyperedge HE-7 (induced by source paper "data driven unsupervised clustering of online learner behaviour"):
  with members:
    - "likely to stop predicting stopout in massive open online courses"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"

Connected through hyperedge HE-8 (induced by source paper "sparsification of the alignment path search space in dynamic time warping"):
  with members:
    - "times series averaging and denoising from a probabilistic perspective on time elastic kernels"
    - "on recursive edit distance kernels with application to time series classification"
    - "sparsedtw a novel approach to speed up dynamic time warping"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

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)