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: "exploiting multi typed treebanks for parsing with deep multi task learning"
  Abstract: Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain syntactic knowledge that is potential to benefit each other. This paper presents an universal framework for exploiting these multi-typed treebanks to improve parsing with deep multi-task learning. We consider two kinds of treebanks as source: the multilingual universal treebanks and the monolingual heterogeneous treebanks. Multiple treebanks are trained jointly and interacted with multi-level parameter sharing. Experiments on several benchmark datasets in various languages demonstrate that our approach can make effective use of arbitrary source treebanks to improve target parsing models.

Connected through hyperedge HE-1 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-2 (induced by source paper "multitask parsing across semantic representations"):
  with members:
    - "learning joint semantic parsers from disjoint data"
    - "a transition based directed acyclic graph parser for ucca"
    - "neural amr sequence to sequence models for parsing and generation"
    - "improving generalization performance by switching from adam to sgd"
    - "robust incremental neural semantic graph parsing"
    - "transfer learning for neural semantic parsing"
    - "deep multitask learning for semantic dependency parsing"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "multi task learning over graph structures"):
  with members:
    - "meta learning multi task communication"
    - "neural message passing for quantum chemistry"
    - "deep multitask learning for semantic dependency parsing"
    - "long short term memory networks for machine reading"
    - "semi supervised classification with graph convolutional networks"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "multi task cross lingual sequence tagging from scratch"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-4 (induced by source paper "universal unsupervised uncovered sentiment analysis"):
  with members:
    - "many languages one parser"
    - "one model two languages training bilingual parsers with harmonized treebanks"
    - "a convolutional neural network for modelling sentences"
    - "a universal part of speech tagset"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

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)