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: "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.

Connected through hyperedge HE-1 (induced by source paper "query by example on device keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "attention based end to end models for small footprint keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "meta learning for few shot keyword spotting"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "end to end asr free keyword search from speech"
    - "trainable frontend for robust and far field keyword spotting"

Connected through hyperedge HE-2 (induced by source paper "classifying topics in speech when all you have is crummy translations"):
  with members:
    - "visually grounded cross lingual keyword spotting in speech"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "towards speech to text translation without speech recognition"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-3 (induced by source paper "asr free cnn dtw keyword spotting using multilingual bottleneck features for almost zero resource languages"):
  with members:
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "multilingual bottleneck features for subword modeling in zero resource languages"
    - "end to end asr free keyword search from speech"
    - "very deep convolutional networks for end to end speech recognition"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"

Connected through hyperedge HE-4 (induced by source paper "feature trajectory dynamic time warping for clustering of speech segments"):
  with members:
    - "asr free cnn dtw keyword spotting using multilingual bottleneck features for almost zero resource languages"
    - "methods of hierarchical clustering"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

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)