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: "small footprint keyword spotting using deep neural network and connectionist temporal classifier"
  Abstract: Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount. DNN is to directly predict the posterior of phoneme units of any personally customized key-phrase, and CTC to produce a confidence score of the given phoneme sequence as responsive decision-making mechanism. The CTC-KWS has competitive performance in comparison with purely DNN based keyword specific KWS, but not increasing any computational complexity.

Connected through hyperedge HE-1 (induced by source paper "learning to detect keyword parts and whole by smoothed max pooling"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "end to end streaming keyword spotting"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "a cascade architecture for keyword spotting on mobile devices"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"

Connected through hyperedge HE-2 (induced by source paper "temporal convolution for real time keyword spotting on mobile devices"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mnasnet platform aware neural architecture search for mobile"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-3 (induced by source paper "end to end streaming keyword spotting"):
  with members:
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "a cascade architecture for keyword spotting on mobile devices"
    - "feedforward sequential memory networks a new structure to learn long term dependency"

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.CL(Computation and Language)