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: "multivariate time series classification using dynamic time warping template selection for human activity recognition"
  Abstract: Accurate and computationally efficient means for classifying human activities have been the subject of extensive research efforts. Most current research focuses on extracting complex features to achieve high classification accuracy. We propose a template selection approach based on Dynamic Time Warping, such that complex feature extraction and domain knowledge is avoided. We demonstrate the predictive capability of the algorithm on both simulated and real smartphone data.

Connected through hyperedge HE-1 (induced by source paper "multi label prediction in time series data using deep neural networks"):
  with members:
    - "multi label methods for prediction with sequential data"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "towards a fast steady state visual evoked potentials ssvep brain computer interface bci"):
  with members:
    - "compact convolutional neural networks for classification of asynchronous steady state visual evoked potentials"

Connected through hyperedge HE-3 (induced by source paper "multivariate lstm fcns for time series classification"):
  with members:
    - "lstm fully convolutional networks for time series classification"
    - "multivariate time series classification with weasel muse"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "ultra fast shapelets for time series classification"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "time2graph revisiting time series modeling with dynamic shapelets"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "deepwalk online learning of social representations"
    - "a time series forest for classification and feature extraction"

Connected through hyperedge HE-5 (induced by source paper "continual learning augmented investment decisions"):
  with members:
    - "prototypical networks for few shot learning"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "matching networks for one shot learning"
    - "learning to diagnose with lstm recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.AI(Artificial Intelligence)