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: "n gram new groningen author profiling model"
  Abstract: We describe our participation in the PAN 2017 shared task on Author Profiling, identifying authors' gender and language variety for English, Spanish, Arabic and Portuguese. We describe both the final, submitted system, and a series of negative results. Our aim was to create a single model for both gender and language, and for all language varieties. Our best-performing system (on cross-validated results) is a linear support vector machine (SVM) with word unigrams and character 3- to 5-grams as features. A set of additional features, including POS tags, additional datasets, geographic entities, and Twitter handles, hurt, rather than improve, performance. Results from cross-validation indicated high performance overall and results on the test set confirmed them, at 0.86 averaged accuracy, with performance on sub-tasks ranging from 0.68 to 0.98.

Connected through hyperedge HE-1 (induced by source paper "listening between the lines learning personal attributes from conversations"):
  with members:
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "hierarchical recurrent attention network for response generation"

Connected through hyperedge HE-2 (induced by source paper "a comparative analysis of distributional term representations for author profiling in social media"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-3 (induced by source paper "an automated text categorization framework based on hyperparameter optimization"):
  with members:
    - "universal unsupervised rule based uncovered sentiment analysis"
    - "multilingual twitter sentiment classification the role of human annotators"
    - "distributed representations of words and phrases and their compositionality"
    - "machine learning in automated text categorization"
    - "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"

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)