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: "concatenated p mean word embeddings as universal cross lingual sentence representations"
  Abstract: Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. An important advantage of average word embeddings is their computational and conceptual simplicity. However, they typically fall short of the performances of more complex models such as InferSent. Here, we generalize the concept of average word embeddings to p-mean word embeddings, which are (almost) as efficiently computable. We show that the concatenation of different types of p-mean word embeddings considerably closes the gap to state-of-the-art methods such as InferSent monolingually and substantially outperforms these more complex techniques cross-lingually. In addition, our proposed method outperforms different recently proposed baselines such as SIF and Sent2Vec by a solid margin, thus constituting a much harder-to-beat monolingual baseline for a wide variety of transfer tasks. Our data and code are publicly available.

Connected through hyperedge HE-1 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models"):
  with members:
    - "are sixteen heads really better than one"
    - "language models as knowledge bases"
    - "pitfalls in the evaluation of sentence embeddings"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "albert a lite bert for self supervised learning of language representations"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"

Connected through hyperedge HE-2 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings"):
  with members:
    - "a primer in bertology what we know about how bert works"
    - "efficient sentence embedding using discrete cosine transform"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"

Connected through hyperedge HE-3 (induced by source paper "multilingual alignment of contextual word representations"):
  with members:
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "a survey of cross lingual word embedding models"
    - "how multilingual is multilingual bert"
    - "context aware cross lingual mapping"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-4 (induced by source paper "efficient sentence embedding via semantic subspace analysis"):
  with members:
    - "sbert wk a sentence embedding method by dissecting bert based word models"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vector of locally aggregated word embeddings vlawe a novel document level representation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-5 (induced by source paper "investigating an approach for low resource language dataset creation curation and classification setswana and sepedi"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "improving short text classification through global augmentation methods"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "enriching word vectors with subword information"
    - "transfer learning for low resource neural machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-7 (induced by source paper "moverscore text generation evaluating with contextualized embeddings and earth mover distance"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "bertscore evaluating text generation with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "linguistic knowledge and transferability of contextual representations"
    - "better rewards yield better summaries learning to summarise without references"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a simple theoretical model of importance for summarization"

Connected through hyperedge HE-8 (induced by source paper "emu enhancing multilingual sentence embeddings with semantic specialization"):
  with members:
    - "sentence bert sentence embeddings using siamese bert networks"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multi lingual common semantic space construction via cluster consistent word embedding"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)