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: "learning to embed words in context for syntactic tasks"
  Abstract: We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.

Connected through hyperedge HE-1 (induced by source paper "benchmarking approximate inference methods for neural structured prediction"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "design challenges and misconceptions in neural sequence labeling"
    - "learning approximate inference networks for structured prediction"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "classical structured prediction losses for sequence to sequence learning"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "improving joint training of inference networks and structured prediction energy networks"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "spectral normalization for generative adversarial networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning approximate inference networks for structured prediction"
    - "improved training of wasserstein gans"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "deep generative model for joint alignment and word representation"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "generating sentences from a continuous space"
    - "automatic differentiation in machine learning a survey"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"
    - "embedding word similarity with neural machine translation"
    - "multilingual models for compositional distributed semantics"

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)