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: "multi task multi domain representation learning for sequence tagging"
  Abstract: Representation learning with deep models have demonstrated success in a range of NLP. In this paper we consider its use in a multi-task multi-domain setting for sequence tagging by proposing a unified framework for learning across tasks and domains. Our model learns robust representations that yield better performance in this setting. We use shared CRFs and domain projections to allow the model to learn domain specific representations that can feed a single task specific CRF. We evaluate our model on two tasks -- Chinese word segmentation and named entity recognition -- and two domains -- news and social media -- and achieve state-of-the-art results for both social media tasks.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"

Connected through hyperedge HE-2 (induced by source paper "joint multi domain learning for automatic short answer grading"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial multi task learning for text classification"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "deep multitask learning for semantic dependency parsing"
    - "multi task cross lingual sequence tagging from scratch"

Connected through hyperedge HE-3 (induced by source paper "neural end to end learning for computational argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "progressive neural networks"
    - "parsing argumentation structures in persuasive essays"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "dependency parsing as head selection"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-4 (induced by source paper "actionable email intent modeling with reparametrized rnns"):
  with members:
    - "a persona based neural conversation model"
    - "smart reply automated response suggestion for email"
    - "multi task cross lingual sequence tagging from scratch"
    - "many languages one parser"
    - "a neural attention model for abstractive sentence summarization"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"):
  with members:
    - "enriching word vectors with subword information"
    - "a new recurrent neural crf for learning non linear edge features"
    - "charagram embedding words and sentences via character n grams"
    - "bidirectional lstm crf models for sequence tagging"
    - "segmental recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

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)