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: "semi supervised model training for unbounded conversational speech recognition"
  Abstract: For conversational large-vocabulary continuous speech recognition (LVCSR) tasks, up to about two thousand hours of audio is commonly used to train state of the art models. Collection of labeled conversational audio however, is prohibitively expensive, laborious and error-prone. Furthermore, academic corpora like Fisher English (2004) or Switchboard (1992) are inadequate to train models with sufficient accuracy in the unbounded space of conversational speech. These corpora are also timeworn due to dated acoustic telephony features and the rapid advancement of colloquial vocabulary and idiomatic speech over the last decades. Utilizing the colossal scale of our unlabeled telephony dataset, we propose a technique to construct a modern, high quality conversational speech training corpus on the order of hundreds of millions of utterances (or tens of thousands of hours) for both acoustic and language model training. We describe the data collection, selection and training, evaluating the results of our updated speech recognition system on a test corpus of 7K manually transcribed utterances. We show relative word error rate (WER) reductions of {35%, 19%} on {agent, caller} utterances over our seed model and 5% absolute WER improvements over IBM Watson STT on this conversational speech task.

Connected through hyperedge HE-1 (induced by source paper "lattice based unsupervised test time adaptation of neural network acoustic models"):
  with members:
    - "dnn adaptation by automatic quality estimation of asr hypotheses"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"

Connected through hyperedge HE-2 (induced by source paper "the marchex 2018 english conversational telephone speech recognition system"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "the ibm 2015 english conversational telephone speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "audio spectrogram factorization for classification of telephony signals below the auditory threshold"):
  with members:
    - "riddim a rhythm analysis and decomposition tool based on independent subspace analysis"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "a neural network alternative to non negative audio models"

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)