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: "automated audio captioning with recurrent neural networks"
  Abstract: We present the first approach to automated audio captioning. We employ an encoder-decoder scheme with an alignment model in between. The input to the encoder is a sequence of log mel-band energies calculated from an audio file, while the output is a sequence of words, i.e. a caption. The encoder is a multi-layered, bi-directional gated recurrent unit (GRU) and the decoder a multi-layered GRU with a classification layer connected to the last GRU of the decoder. The classification layer and the alignment model are fully connected layers with shared weights between timesteps. The proposed method is evaluated using data drawn from a commercial sound effects library, ProSound Effects. The resulting captions were rated through metrics utilized in machine translation and image captioning fields. Results from metrics show that the proposed method can predict words appearing in the original caption, but not always correctly ordered.

Connected through hyperedge HE-1 (induced by source paper "audio caption listen and tell"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-2 (induced by source paper "what does a car ssette tape tell"):
  with members:
    - "audio caption listen and tell"
    - "universal dependency parsing from scratch"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learned in translation contextualized word vectors"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"

Connected through hyperedge HE-3 (induced by source paper "clotho an audio captioning dataset"):
  with members:
    - "audio caption listen and tell"
    - "crowdsourcing a dataset of audio captions"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing a dataset of audio captions"):
  with members:
    - "audio caption listen and tell"
    - "microsoft coco captions data collection and evaluation server"
    - "deep visual semantic alignments for generating image descriptions"

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)