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: "parsing tweets into universal dependencies"
  Abstract: We study the problem of analyzing tweets with Universal Dependencies. We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. Using the extended guidelines, we create a new tweet treebank for English (Tweebank v2) that is four times larger than the (unlabeled) Tweebank v1 introduced by Kong et al. (2014). We characterize the disagreements between our annotators and show that it is challenging to deliver consistent annotation due to ambiguity in understanding and explaining tweets. Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD. To overcome annotation noise without sacrificing computational efficiency, we propose a new method to distill an ensemble of 20 transition-based parsers into a single one. Our parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and outperforms parsers that are state-of-the-art on other treebanks in both accuracy and speed.

Connected through hyperedge HE-1 (induced by source paper "joint learning of pre trained and random units for domain adaptation in part of speech tagging"):
  with members:
    - "revisiting the importance of individual units in cnns via ablation"
    - "deep contextualized word representations"
    - "interpreting deep visual representations via network dissection"
    - "learning more universal representations for transfer learning"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "how transferable are neural networks in nlp applications"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-2 (induced by source paper "dependency parsing for spoken dialog systems"):
  with members:
    - "all roads lead to ud converting stanford and penn parses to english universal dependencies with multilayer annotations"
    - "universal dependency parsing from scratch"
    - "parser training with heterogeneous treebanks"
    - "semi supervised sequence modeling with cross view training"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-3 (induced by source paper "multi source domain adaptation with mixture of experts"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "aspect augmented adversarial networks for domain adaptation"
    - "prototypical networks for few shot learning"
    - "learning to select data for transfer learning with bayesian optimization"
    - "knowledge adaptation teaching to adapt"
    - "matching networks for one shot learning"
    - "neural structural correspondence learning for domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"):
  with members:
    - "distilling knowledge for search based structured prediction"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "neural probabilistic model for non projective mst parsing"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "cross lingual word segmentation and morpheme segmentation as sequence labelling"
    - "enriching word vectors with subword information"
    - "many languages one parser"
    - "on using very large target vocabulary for neural machine translation"

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)