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 "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"):
  Abstract: We use dynamic time warping (DTW) as supervision for training a convolutional neural network (CNN) based keyword spotting system using a small set of spoken isolated keywords. The aim is to allow rapid deployment of a keyword spotting system in a new language to support urgent United Nations (UN) relief programmes in parts of Africa where languages are extremely under-resourced and the development of annotated speech resources is infeasible. First, we use 1920 recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a DTW-based template matching system and apply it to untranscribed broadcast speech. Then, we use the resulting DTW scores as targets to train a CNN on the same unlabelled speech. In this way we use just 34 minutes of labelled speech, but leverage a large amount of unlabelled data for training. While the resulting CNN keyword spotter cannot match the performance of the DTW-based system, it substantially outperforms a CNN classifier trained only on the keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our CNN system is several orders of magnitude faster at runtime than the DTW system, it represents the most viable keyword spotter on this extremely limited dataset.

Includes members:
  Member "end to end asr free keyword search from speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "very deep convolutional networks for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "discriminative acoustic word embeddings recurrent neural network based approaches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving automated segmentation of radio shows with audio embeddings")
  Member "empirical evaluation of rectified activations in convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "soft root sign activation function")
  Member "deep convolutional acoustic word embeddings using word pair side information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.CL(Computation and Language)