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: "sound word2vec learning word representations grounded in sounds"
  Abstract: To be able to interact better with humans, it is crucial for machines to understand sound - a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic textual similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream textual tasks which require aural grounding. To this end, we propose sound-word2vec - a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we learn that two seemingly (semantically) unrelated concepts, like leaves and paper are similar due to the similar rustling sounds they make. Our embeddings prove useful in textual tasks requiring aural reasoning like text-based sound retrieval and discovering foley sound effects (used in movies). Moreover, our embedding space captures interesting dependencies between words and onomatopoeia and outperforms prior work on aurally-relevant word relatedness datasets such as AMEN and ASLex.

Connected through hyperedge HE-1 (induced by source paper "improving automated segmentation of radio shows with audio embeddings"):
  with members:
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-2 (induced by source paper "word2vec to behavior morphology facilitates the grounding of language in machines"):
  with members:
    - "automated shapeshifting for function recovery in damaged robots"
    - "learning agile and dynamic motor skills for legged robots"
    - "grasp2vec learning object representations from self supervised grasping"
    - "morphology dictates a robot s ability to ground crowd proposed language"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "improving visually grounded sentence representations with self attention"):
  with members:
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "efficient vector representation for documents through corruption"
    - "rethinking skip thought a neighborhood based approach"
    - "emergent translation in multi agent communication"
    - "deconvolutional paragraph representation learning"
    - "a structured self attentive sentence embedding"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-4 (induced by source paper "visually grounded learning of keyword prediction from untranscribed speech"):
  with members:
    - "visual features for context aware speech recognition"
    - "learning word like units from joint audio visual analysis"
    - "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)