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: "visual features for context aware speech recognition"
  Abstract: Automatic transcriptions of consumer generated multi-media content such as “Youtube” videos still exhibit high word error rates. Such data typically occupies a very broad domain, has been recorded in challenging conditions, with cheap hardware and a focus on the visual modality, and may have been post-processed or edited.

Connected through hyperedge HE-1 (induced by source paper "looking enhances listening recovering missing speech using images"):
  with members:
    - "how2 a large scale dataset for multimodal language understanding"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "end to end attention based large vocabulary speech recognition"
    - "deep residual learning for image recognition"
    - "deep multimodal semantic embeddings for speech and images"

Connected through hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "sequence to sequence models can directly translate foreign speech"
    - "learning word like units from joint audio visual analysis"
    - "learning modality invariant representations for speech and images"
    - "unsupervised neural and bayesian models for zero resource speech processing"

Connected through hyperedge HE-3 (induced by source paper "analyzing utility of visual context in multimodal speech recognition under noisy conditions"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "modality attention for end to end audio visual speech recognition"
    - "how2 a large scale dataset for multimodal language understanding"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention strategies for multi source sequence to sequence learning"
    - "lipnet end to end sentence level lipreading"
    - "lip reading sentences in the wild"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-4 (induced by source paper "multimodal language analysis with recurrent multistage fusion"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal local global ranking fusion for emotion recognition"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "reusing neural speech representations for auditory emotion recognition"
    - "learning factorized multimodal representations"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "tensor fusion network for multimodal sentiment analysis"

Connected through hyperedge HE-5 (induced by source paper "multimodal grounding for sequence to sequence speech recognition"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "how2 a large scale dataset for multimodal language understanding"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "lip reading sentences in the wild"
    - "using the output embedding to improve language models"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "unspeech unsupervised speech context embeddings"):
  with members:
    - "neural predictive coding using convolutional neural networks toward unsupervised learning of speaker characteristics"
    - "unsupervised learning of semantic audio representations"
    - "context encoders feature learning by inpainting"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "very deep convolutional networks for large scale image recognition"
    - "parallel training of dnns with natural gradient and parameter averaging"
    - "weakly supervised multi embeddings learning of acoustic models"

Connected through hyperedge HE-7 (induced by source paper "deep learning based multi modal addressee recognition in visual scenes with utterances"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-8 (induced by source paper "visually grounded learning of keyword prediction from untranscribed speech"):
  with members:
    - "learning word like units from joint audio visual analysis"
    - "sound word2vec learning word representations grounded in sounds"
    - "representations of language in a model of visually grounded speech signal"
    - "towards speech to text translation without speech recognition"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "ambient sound provides supervision for visual learning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "soundnet learning sound representations from unlabeled video"

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)