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: "auxiliary objectives for neural error detection models"
  Abstract: We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.

Connected through hyperedge HE-1 (induced by source paper "pretraining methods for dialog context representation learning"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-2 (induced by source paper "context is key grammatical error detection with contextual word representations"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "artificial error generation with machine translation and syntactic patterns"

Connected through hyperedge HE-3 (induced by source paper "multi task learning for coherence modeling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "discourse coherence in the wild a dataset evaluation and methods"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for chinese word usage errors detection"):
  with members:
    - "multi task learning of keyphrase boundary classification"
    - "semi supervised multitask learning for sequence labeling"
    - "adversarial multi task learning for text classification"
    - "an overview of multi task learning in deep neural networks"
    - "compositional sequence labeling models for error detection in learner writing"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "multi task cross lingual sequence tagging from scratch"

Connected through hyperedge HE-5 (induced by source paper "numeracy for language models evaluating and improving their ability to predict numbers"):
  with members:
    - "a joint model for question answering and question generation"
    - "a neural knowledge language model"
    - "gated word character recurrent language model"
    - "neural text generation from structured data with application to the biography domain"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "annotating derivations a new evaluation strategy and dataset for algebra word problems"
    - "clinical text prediction with numerically grounded conditional language models"
    - "solving general arithmetic word problems"

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)