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: "comparing human and machine errors in conversational speech transcription"
  Abstract: Recent work in automatic recognition of conversational telephone speech (CTS) has achieved accuracy levels comparable to human transcribers, although there is some debate how to precisely quantify human performance on this task, using the NIST 2000 CTS evaluation set. This raises the question what systematic differences, if any, may be found differentiating human from machine transcription errors. In this paper we approach this question by comparing the output of our most accurate CTS recognition system to that of a standard speech transcription vendor pipeline. We find that the most frequent substitution, deletion and insertion error types of both outputs show a high degree of overlap. The only notable exception is that the automatic recognizer tends to confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans tend not to make this error, presumably due to the distinctive and opposing pragmatic functions attached to these words. Furthermore, we quantify the correlation between human and machine errors at the speaker level, and investigate the effect of speaker overlap between training and test data. Finally, we report on an informal "Turing test" asking humans to discriminate between automatic and human transcription error cases.

Connected through hyperedge HE-1 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr"):
  with members:
    - "advancing acoustic to word ctc model"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "acoustic to word model without oov"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-2 (induced by source paper "natural language interactions in autonomous vehicles intent detection and slot filling from passenger utterances"):
  with members:
    - "conversational intent understanding for passengers in autonomous vehicles"
    - "key value retrieval networks for task oriented dialogue"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "bag of tricks for efficient text classification"
    - "achieving human parity in conversational speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "disfluencies and human speech transcription errors"):
  with members:
    - "giving attention to the unexpected using prosody innovations in disfluency detection"
    - "robust cross domain disfluency detection with pattern match networks"
    - "english conversational telephone speech recognition by humans and machines"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "disfluency detection using a bidirectional lstm"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-4 (induced by source paper "challenging the boundaries of speech recognition the malach corpus"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "state of the art speech recognition with sequence to sequence models"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-5 (induced by source paper "the capio 2017 conversational speech recognition system"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "the microsoft 2017 conversational speech recognition system"
    - "an improved residual lstm architecture for acoustic modeling"
    - "language modeling with highway lstm"
    - "sequence training and adaptation of highway deep neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "toward domain invariant speech recognition via large scale training"):
  with members:
    - "generalizing across domains via cross gradient training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "adversarial discriminative domain adaptation"
    - "large scale domain adaptation via teacher student learning"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "memory fusion network for multi view sequential learning"
    - "conversational ai the science behind the alexa prize"
    - "multimodal sentiment analysis addressing key issues and setting up baselines"
    - "tensor fusion network for multimodal sentiment analysis"
    - "adversarial examples for evaluating reading comprehension systems"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "automatic sarcasm detection a survey"

Connected through hyperedge HE-8 (induced by source paper "streaming voice query recognition using causal convolutional recurrent neural networks"):
  with members:
    - "deep residual learning for small footprint keyword spotting"
    - "state of the art speech recognition with sequence to sequence models"
    - "learning intrinsic sparse structures within long short term memory"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "listening to the world improves speech command recognition"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the microsoft 2016 conversational speech recognition system"

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)