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 "asr free cnn dtw keyword spotting using multilingual bottleneck features for almost zero resource languages"):
  Abstract: We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting. This forms part of a United Nations effort using keyword spotting to support humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We use 1920 isolated keywords (40 types, 34 minutes) as exemplars for dynamic time warping (DTW) template matching, which is performed on a much larger body of untranscribed speech. These DTW costs are used as targets for a convolutional neural network (CNN) keyword spotter, giving a much faster system than direct DTW. Here we consider how available data from well-resourced languages can improve this CNN-DTW approach. We show that multilingual BNFs trained on ten languages improve the area under the ROC curve of a CNN-DTW system by 10.9% absolute relative to the MFCC baseline. By combining low-resource DTW-based supervision with information from well-resourced languages, CNN-DTW is a competitive option for low-resource keyword spotting.

Includes members:
  Member "acoustic and textual data augmentation for improved asr of code switching speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition")
  Member "speech2vec a sequence to sequence framework for learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "learning acoustic word embeddings with temporal context for query by example speech search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "multilingual bottleneck features for subword modeling in zero resource languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards zero shot learning for automatic phonemic transcription")
  Member "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "query by example on device keyword spotting")
  Member "end to end asr free keyword search from speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")

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)