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: "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
  Abstract: Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with an adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages and on three tasks: word similarity, dialog state tracking, and lexical simplification. We report consistent improvements over distributional word vectors and vectors specialized by other state-of-the-art specialization frameworks. Finally, we also propose a cross-lingual transfer method for zero-shot specialization which successfully specializes a full target distributional space without any lexical knowledge in the target language and without any bilingual data.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "a general framework for implicit and explicit debiasing of distributional word vector spaces"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "are we consistently biased multidimensional analysis of biases in distributional word vectors"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "attenuating bias in word vectors"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-3 (induced by source paper "informing unsupervised pretraining with external linguistic knowledge"):
  with members:
    - "towards universal dialogue state tracking"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network acceptability judgments"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "attention is all you need"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-4 (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"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "deep transfer reinforcement learning for text summarization"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-6 (induced by source paper "imparting interpretability to word embeddings while preserving semantic structure"):
  with members:
    - "analytical methods for interpretable ultradense word embeddings"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "spine sparse interpretable neural embeddings"
    - "jointly learning word embeddings and latent topics"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"

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)