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: "high risk learning acquiring new word vectors from tiny data"
  Abstract: Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn 'a good vector' for a word, a model must have sufficient examples of its usage. This contradicts the fact that humans can guess the meaning of a word from a few occurrences only. In this paper, we show that a neural language model such as Word2Vec only necessitates minor modifications to its standard architecture to learn new terms from tiny data, using background knowledge from a previously learnt semantic space. We test our model on word definitions and on a nonce task involving 2-6 sentences' worth of context, showing a large increase in performance over state-of-the-art models on the definitional task.

Connected through hyperedge HE-1 (induced by source paper "learning to learn words from visual scenes"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "pointing novel objects in image captioning"

Connected through hyperedge HE-2 (induced by source paper "a systematic study of leveraging subword information for learning word representations"):
  with members:
    - "morphological word embeddings"
    - "learning semantic representations for novel words leveraging both form and context"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "from characters to words to in between do we capture morphology"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"

Connected through hyperedge HE-3 (induced by source paper "learning to learn words from narrated video"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "bertram improved word embeddings have big impact on contextualized model performance"):
  with members:
    - "bad form comparing context based and form based few shot learning in distributional semantic models"
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "rare words a major problem for contextualized embeddings and how to fix it by attentive mimicking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "incorporating subword information into matrix factorization word embeddings"
    - "learning semantic representations for novel words leveraging both form and context"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "rare words a major problem for contextualized embeddings and how to fix it by attentive mimicking"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "huggingface s transformers state of the art natural language processing"
    - "incorporating subword information into matrix factorization word embeddings"
    - "learning semantic representations for novel words leveraging both form and context"
    - "the natural language decathlon multitask learning as question answering"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"

Connected through hyperedge HE-6 (induced by source paper "few shot representation learning for out of vocabulary words"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "one shot relational learning for knowledge graphs"
    - "diverse few shot text classification with multiple metrics"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "mimicking word embeddings using subword rnns"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "attentive mimicking better word embeddings by attending to informative contexts"):
  with members:
    - "incorporating subword information into matrix factorization word embeddings"
    - "evaluating word embeddings in multi label classification using fine grained name typing"
    - "learning semantic representations for novel words leveraging both form and context"
    - "attention is all you need"
    - "mimicking word embeddings using subword rnns"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "enriching word vectors with subword information"
    - "ultradense word embeddings by orthogonal transformation"

Connected through hyperedge HE-8 (induced by source paper "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain"):
  with members:
    - "redefining context windows for word embedding models an experimental study"
    - "the effects of data size and frequency range on distributional semantic models"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "issues in evaluating semantic spaces using word analogies"
    - "mining and modeling character networks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

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)