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 learning of keyphrase boundary classification"
  Abstract: Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for chinese word usage errors detection"):
  with members:
    - "semi supervised multitask learning for sequence labeling"
    - "auxiliary objectives for neural error detection models"
    - "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-2 (induced by source paper "dynamic mode decomposition based feature for image classification"):
  with members:
    - "how transferable are the datasets collected by active learners"
    - "autoaugment learning augmentation policies from data"
    - "universal language model fine tuning for text classification"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "snorkel rapid training data creation with weak supervision"
    - "learning from simulated and unsupervised images through adversarial training"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "scientific information extraction with semi supervised neural tagging"):
  with members:
    - "lstm based conversation models"
    - "neural architectures for named entity recognition"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "bidirectional lstm crf models for sequence tagging"
    - "semi supervised sequence learning"
    - "named entity recognition with bidirectional lstm cnns"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)