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: "cross language learning with adversarial neural networks application to community question answering"
  Abstract: We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.

Connected through hyperedge HE-1 (induced by source paper "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"):
  with members:
    - "cross lingual language model pretraining"
    - "a corpus for multilingual document classification in eight languages"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a neural interlingua for multilingual machine translation"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"

Connected through hyperedge HE-2 (induced by source paper "detecting cognitive impairments by agreeing on interpretations of linguistic features"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "one ruler for all languages multi lingual dialogue evaluation with adversarial multi task learning"
    - "enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts"
    - "on the importance of normative data in speech based assessment"
    - "unified deep supervised domain adaptation and generalization"
    - "adversarial multi task learning for text classification"
    - "unsupervised cross domain image generation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "adversarial neural networks for cross lingual sequence tagging"):
  with members:
    - "adversarial generation of natural language"
    - "adversarial learning for neural dialogue generation"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "improved training of wasserstein gans"
    - "robust multilingual part of speech tagging via adversarial training"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "improving sentence compression by learning to predict gaze"
    - "multilingual word embeddings using multigraphs"

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)