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: "tnt a statistical part of speech tagger"
  Abstract: Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.

Connected through hyperedge HE-1 (induced by source paper "neural cross lingual transfer and limited annotated data for named entity recognition in danish"):
  with members:
    - "multilingual ner transfer for low resource languages"
    - "word translation without parallel data"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "polyglot distributed word representations for multilingual nlp"
    - "natural language processing almost from scratch"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "metric learning for dynamic text classification"):
  with members:
    - "hyperbolic interaction model for hierarchical multi label classification"
    - "hyperbolic image embeddings"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "multi source domain adaptation with mixture of experts"
    - "deep contextualized word representations"
    - "hyperbolic neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "i am not what i write privacy preserving text representation learning"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "privacy in social media identification mitigation and applications"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"
    - "semi supervised learning for detecting human trafficking"

Connected through hyperedge HE-4 (induced by source paper "natural language understanding with the quora question pairs dataset"):
  with members:
    - "a decomposable attention model for natural language inference"
    - "how transferable are neural networks in nlp applications"
    - "effective approaches to attention based neural machine translation"
    - "natural language inference by tree based convolution and heuristic matching"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "formal models of structure building in music language and animal songs"):
  with members:
    - "bias and agreement in syntactic annotations"
    - "gated feedback recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "analyzing linguistic complexity and scientific impact"):
  with members:
    - "examining scientific writing styles from the perspective of linguistic complexity"
    - "viziometrics analyzing visual information in the scientific literature"
    - "a review of the literature on citation impact indicators"
    - "caveats for using statistical significance tests in research assessments"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-7 (induced by source paper "feature rich part of speech tagging for morphologically complex languages application to bulgarian"):
  with members:
    - "alternating projections for learning with expectation constraints"

Connected through hyperedge HE-8 (induced by source paper "a hierarchical deep learning natural language parser for fashion"):
  with members:
    - "enhanced lstm for natural language inference"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "how transferable are neural networks in nlp applications"
    - "bag of tricks for efficient text classification"
    - "transition based dependency parsing with stack long short term memory"
    - "named entity recognition with bidirectional lstm cnns"
    - "rethinking the inception architecture for computer vision"

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)