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: "parser training with heterogeneous treebanks"
  Abstract: How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question. We start by investigating previously suggested, but little evaluated, strategies for exploiting multiple treebanks based on concatenating training sets, with or without fine-tuning. We go on to propose a new method based on treebank embeddings. We perform experiments for several languages and show that in many cases fine-tuning and treebank embeddings lead to substantial improvements over single treebanks or concatenation, with average gains of 2.0--3.5 LAS points. We argue that treebank embeddings should be preferred due to their conceptual simplicity, flexibility and extensibility.

Connected through hyperedge HE-1 (induced by source paper "a systematic comparison of methods for low resource dependency parsing on genuinely low resource languages"):
  with members:
    - "data augmentation via dependency tree morphing for low resource languages"
    - "parameter sharing between dependency parsers for related languages"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "universal language model fine tuning for text classification"
    - "data augmentation for low resource neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"

Connected through hyperedge HE-2 (induced by source paper "cross lingual parsing with polyglot training and multi treebank learning a faroese case study"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "cross lingual dependency parsing with late decoding for truly low resource languages"

Connected through hyperedge HE-3 (induced by source paper "learning word embeddings with domain awareness"):
  with members:
    - "factors influencing the surprising instability of word embeddings"
    - "on the dimensionality of word embedding"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"
    - "bag of tricks for efficient text classification"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "compositional semantic parsing across graphbanks"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "learning joint semantic parsers from disjoint data"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "simpler but more accurate semantic dependency parsing"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-5 (induced by source paper "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"):
  with members:
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "multitask parsing across semantic representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a transition based directed acyclic graph parser for ucca"
    - "a minimal span based neural constituency parser"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-6 (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"
    - "parsing tweets into universal dependencies"
    - "semi supervised sequence modeling with cross view training"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "deep biaffine attention for neural dependency parsing"

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)