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 speech and keyboard text entry for short messages in two languages on touchscreen phones"
  Abstract: With the ubiquity of mobile touchscreen devices like smartphones, two widely used text entry methods have emerged: small touch-based keyboards and speech recognition. Although speech recognition has been available on desktop computers for years, it has continued to improve at a rapid pace, and it is currently unknown how today's modern speech recognizers compare to state-of-the-art mobile touch keyboards, which also have improved considerably since their inception. To discover both methods' “upper-bound performance,” we evaluated them in English and Mandarin Chinese on an Apple iPhone 6 Plus in a laboratory setting. Our experiment was carried out using Baidu's Deep Speech 2, a deep learning-based speech recognition system, and the built-in Qwerty (English) or Pinyin (Mandarin) Apple iOS keyboards. We found that with speech recognition, the English input rate was 2.93 times faster (153 vs. 52 WPM), and the Mandarin Chinese input rate was 2.87 times faster (123 vs. 43 WPM) than the keyboard for short message transcription under laboratory conditions for both methods. Furthermore, although speech made fewer errors during entry (5.30% vs. 11.22% corrected error rate), it left slightly more errors in the final transcribed text (1.30% vs. 0.79% uncorrected error rate). Our results show that comparatively, under ideal conditions for both methods, upper-bound speech recognition performance has greatly improved compared to prior systems, and might see greater uptake in the future, although further study is required to quantify performance in non-laboratory settings for both methods.

Connected through hyperedge HE-1 (induced by source paper "mobile text entry behaviour in lab and in the wild studies is it different"):
  with members:
    - "coverage of highly cited documents in google scholar web of science and scopus a multidisciplinary comparison"

Connected through hyperedge HE-2 (induced by source paper "smart home personal assistants a security and privacy review"):
  with members:
    - "intelligent virtual assistant knows your life"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "contextual slot carryover for disparate schemas"
    - "user perceptions of smart home iot privacy"
    - "understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "continuous authentication for voice assistants"

Connected through hyperedge HE-3 (induced by source paper "a survey investigating usage of virtual personal assistants"):
  with members:
    - "evaluating personal assistants on mobile devices"
    - "very deep multilingual convolutional neural networks for lvcsr"

Connected through hyperedge HE-4 (induced by source paper "speech based visual question answering"):
  with members:
    - "dual attention networks for multimodal reasoning and matching"
    - "hierarchical question image co attention for visual question answering"
    - "soundnet learning sound representations from unlabeled video"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "simple baseline for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "object referring in visual scene with spoken language"):
  with members:
    - "speech based visual question answering"
    - "representations of language in a model of visually grounded speech signal"
    - "mask r cnn"
    - "phrase localization and visual relationship detection with comprehensive linguistic cues"
    - "segmentation from natural language expressions"
    - "grounding of textual phrases in images by reconstruction"
    - "synthetic data for text localisation in natural images"
    - "modeling context between objects for referring expression understanding"

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.HC(Human-Computer Interaction)