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: "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
  Abstract: The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.

Connected through hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"

Connected through hyperedge HE-2 (induced by source paper "on the importance of word order information in cross lingual sequence labeling"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how multilingual is multilingual bert"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "self attention with relative position representations"

Connected through hyperedge HE-3 (induced by source paper "do we need word order information for cross lingual sequence labeling"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "self attention with relative position representations"

Connected through hyperedge HE-4 (induced by source paper "low resource sequence labeling via unsupervised multilingual contextualized representations"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural cross lingual named entity recognition with minimal resources"
    - "demystifying neural style transfer"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-5 (induced by source paper "rewarding smatch transition based amr parsing with reinforcement learning"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "amr parsing using stack lstms"
    - "neural cross lingual entity linking"

Connected through hyperedge HE-6 (induced by source paper "enhanced meta learning for cross lingual named entity recognition with minimal resources"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "cross lingual language model pretraining"
    - "chinese ner using lattice lstm"
    - "meta learning for low resource neural machine translation"
    - "deep contextualized word representations"
    - "natural language to structured query generation via meta learning"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

Connected through hyperedge HE-7 (induced by source paper "low resource name tagging learned with weakly labeled data"):
  with members:
    - "few shot classification in named entity recognition task"
    - "improving neural relation extraction with implicit mutual relations"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "multi channel graph neural network for entity alignment"
    - "neural collective entity linking"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "few shot learning for named entity recognition in medical text"

Connected through hyperedge HE-8 (induced by source paper "cross lingual transfer for distantly supervised and low resources indonesian ner"):
  with members:
    - "empirical evaluation of character based model on neural named entity recognition in indonesian conversational texts"
    - "toward a standardized and more accurate indonesian part of speech tagging"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "neural cross lingual named entity recognition with minimal resources"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

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)