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: "weakly supervised spoken term discovery using cross lingual side information"
  Abstract: Recent work on unsupervised term discovery (UTD) aims to identify and cluster repeated word-like units from audio alone. These systems are promising for some very low-resource languages where transcribed audio is unavailable, or where no written form of the language exists. However, in some cases it may still be feasible (e.g., through crowdsourcing) to obtain (possibly noisy) text translations of the audio. If so, this information could be used as a source of side information to improve UTD. Here, we present a simple method for rescoring the output of a UTD system using text translations, and test it on a corpus of Spanish audio with English translations. We show that it greatly improves the average precision of the results over a wide range of system configurations and data preprocessing methods.

Connected through hyperedge HE-1 (induced by source paper "large scale representation learning from visually grounded untranscribed speech"):
  with members:
    - "towards visually grounded sub word speech unit discovery"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "benchmark analysis of representative deep neural network architectures"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "efficient natural language response suggestion for smart reply"

Connected through hyperedge HE-2 (induced by source paper "a small griko italian speech translation corpus"):
  with members:
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "part of speech tagging on an endangered language a parallel griko italian resource"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "sequence to sequence models can directly translate foreign speech"
    - "a very low resource language speech corpus for computational language documentation experiments"
    - "towards speech to text translation without speech recognition"
    - "unwritten languages demand attention too word discovery with encoder decoder models"

Connected through hyperedge HE-3 (induced by source paper "unwritten languages demand attention too word discovery with encoder decoder models"):
  with members:
    - "sequence to sequence models can directly translate foreign speech"
    - "a case study on using speech to translation alignments for language documentation"
    - "six challenges for neural machine translation"
    - "fully character level neural machine translation without explicit segmentation"
    - "listen and translate a proof of concept for end to end speech to text translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "towards speech to text translation without speech recognition"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "unsupervised word segmentation and lexicon discovery using acoustic word embeddings"
    - "an unsupervised probability model for speech to translation alignment of low resource languages"
    - "listen and translate a proof of concept for end to end speech to text translation"

Connected through hyperedge HE-5 (induced by source paper "a case study on using speech to translation alignments for language documentation"):
  with members:
    - "an unsupervised probability model for speech to translation alignment of low resource languages"

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)