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: "a comparison of online automatic speech recognition systems and the nonverbal responses to unintelligible speech"
  Abstract: Automatic Speech Recognition (ASR) systems have proliferated over the recent years to the point that free platforms such as YouTube now provide speech recognition services. Given the wide selection of ASR systems, we contribute to the field of automatic speech recognition by comparing the relative performance of two sets of manual transcriptions and five sets of automatic transcriptions (Google Cloud, IBM Watson, Microsoft Azure, Trint, and YouTube) to help researchers to select accurate transcription services. In addition, we identify nonverbal behaviors that are associated with unintelligible speech, as indicated by high word error rates. We show that manual transcriptions remain superior to current automatic transcriptions. Amongst the automatic transcription services, YouTube offers the most accurate transcription service. For non-verbal behavioral involvement, we provide evidence that the variability of smile intensities from the listener is high (low) when the speaker is clear (unintelligible). These findings are derived from videoconferencing interactions between student doctors and simulated patients; therefore, we contribute towards both the ASR literature and the healthcare communication skills teaching community.

Connected through hyperedge HE-1 (induced by source paper "convo what does conversational programming need an exploration of machine learning interface design"):
  with members:
    - "pumice a multi modal agent that learns concepts and conditionals from natural language and demonstrations"
    - "program synthesis and semantic parsing with learned code idioms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "meet cyrus the query by voice mobile assistant for the tutoring and formative assessment of sql learners"
    - "abstract syntax networks for code generation and semantic parsing"
    - "attention based models for speech recognition"

Connected through hyperedge HE-2 (induced by source paper "translation sentiment and voices a computational model to translate and analyse voices from real time video calling"):
  with members:
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "tensor fusion network for multimodal sentiment analysis"
    - "opennmt open source toolkit for neural machine translation"
    - "towards speech to text translation without speech recognition"
    - "recurrent neural network for text classification with multi task learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "linguistic input features improve neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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.SD(Sound)