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: "speech based visual question answering"
  Abstract: This paper introduces speech-based visual question answering (VQA), the task of generating an answer given an image and a spoken question. Two methods are studied: an end-to-end, deep neural network that directly uses audio waveforms as input versus a pipelined approach that performs ASR (Automatic Speech Recognition) on the question, followed by text-based visual question answering. Furthermore, we investigate the robustness of both methods by injecting various levels of noise into the spoken question and find both methods to be tolerate noise at similar levels.

Connected through hyperedge HE-1 (induced by source paper "temporal reasoning via audio question answering"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "clear a dataset for compositional language and elementary acoustic reasoning"
    - "a dataset and architecture for visual reasoning with a working memory"
    - "compositional attention networks for machine reasoning"
    - "tvqa localized compositional video question answering"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-2 (induced by source paper "non monotonic logical reasoning guiding deep learning for explainable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "answering visual what if questions from actions to predicted scene descriptions"
    - "learning conditioned graph structures for interpretable visual question answering"

Connected through hyperedge HE-3 (induced by source paper "from visual to acoustic question answering"):
  with members:
    - "clear a dataset for compositional language and elementary acoustic reasoning"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "film visual reasoning with a general conditioning layer"
    - "deepstory video story qa by deep embedded memory networks"
    - "analyzing the behavior of visual question answering models"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "speech2vec a sequence to sequence framework for learning word embeddings from speech"):
  with members:
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "learning word like units from joint audio visual analysis"
    - "supervised and unsupervised transfer learning for question answering"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "character composition model with convolutional neural networks for dependency parsing on morphologically rich languages"
    - "learning word embeddings from speech"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"

Connected through hyperedge HE-5 (induced by source paper "object referring in visual scene with spoken language"):
  with members:
    - "comparing speech and keyboard text entry for short messages in two languages on touchscreen phones"
    - "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.CL(Computation and Language)