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: "low resource syntactic transfer with unsupervised source reordering"
  Abstract: We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3% over a state-of-the-art method.

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"
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial neural networks for cross lingual sequence tagging"
    - "self attention with relative position representations"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-2 (induced by source paper "target language aware constrained inference for cross lingual dependency parsing"):
  with members:
    - "how multilingual is multilingual bert"
    - "dependency grammar induction with a neural variational transition based parser"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "word translation without parallel data"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-3 (induced by source paper "working hard or hardly working challenges of integrating typology into neural dependency parsers"):
  with members:
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
    - "survey on the use of typological information in natural language processing"
    - "deep biaffine attention for neural dependency parsing"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"

Connected through hyperedge HE-4 (induced by source paper "cross lingual dependency parsing using code mixed treebank"):
  with members:
    - "parameter sharing between dependency parsers for related languages"
    - "design challenges and misconceptions in neural sequence labeling"
    - "cross lingual dependency parsing with late decoding for truly low resource languages"
    - "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"

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)