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: "cross lingual dependency parsing with late decoding for truly low resource languages"
  Abstract: In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependency parser that can be trained to reproduce matrices of edge scores, which can be directly projected across word alignments. We show that our approach to cross-lingual dependency parsing is not only simpler, but also achieves an absolute improvement of 2.25% averaged across 10 languages compared to the previous state of the art.

Connected through hyperedge HE-1 (induced by source paper "cross lingual dependency parsing with unlabeled auxiliary languages"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "cross lingual syntactic transfer through unsupervised adaptation of invertible projections"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "low resource syntactic transfer with unsupervised source reordering"
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial neural networks for cross lingual sequence tagging"
    - "self attention with relative position representations"

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"
    - "parser training with heterogeneous treebanks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "cross lingual dependency parsing using code mixed treebank"):
  with members:
    - "low resource syntactic transfer with unsupervised source reordering"
    - "parameter sharing between dependency parsers for related languages"
    - "design challenges and misconceptions in neural sequence labeling"
    - "universal dependencies parsing for colloquial singaporean english"
    - "deep biaffine attention for neural dependency parsing"
    - "cross lingual syntactic transfer with limited resources"
    - "many languages one parser"

Connected through hyperedge HE-4 (induced by source paper "cross lingual syntactic transfer with limited resources"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "parsing universal dependencies without training"
    - "many languages one parser"
    - "yara parser a fast and accurate dependency parser"

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)