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: "sharing network parameters for crosslingual named entity recognition"
  Abstract: Most state of the art approaches for Named Entity Recognition rely on hand crafted features and annotated corpora. Recently Neural network based models have been proposed which do not require handcrafted features but still require annotated corpora. However, such annotated corpora may not be available for many languages. In this paper, we propose a neural network based model which allows sharing the decoder as well as word and character level parameters between two languages thereby allowing a resource fortunate language to aid a resource deprived language. Specifically, we focus on the case when limited annotated corpora is available in one language ($L_1$) and abundant annotated corpora is available in another language ($L_2$). Sharing the network architecture and parameters between $L_1$ and $L_2$ leads to improved performance in $L_1$. Further, our approach does not require any hand crafted features but instead directly learns meaningful feature representations from the training data itself. We experiment with 4 language pairs and show that indeed in a resource constrained setup (lesser annotated corpora), a model jointly trained with data from another language performs better than a model trained only on the limited corpora in one language.

Connected through hyperedge HE-1 (induced by source paper "label aware double transfer learning for cross specialty medical named entity recognition"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "transfer learning for named entity recognition with neural networks"
    - "domain adaptation for named entity recognition in online media with word embeddings"
    - "multi task domain adaptation for sequence tagging"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised cross lingual word embedding by multilingual neural language models"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"
    - "word translation without parallel data"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "nonsymbolic text representation"):
  with members:
    - "character based neural machine translation"
    - "online segment to segment neural transduction"
    - "a joint model for word embedding and word morphology"
    - "single model encoder decoder with explicit morphological representation for reinflection"
    - "gated word character recurrent language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

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)