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: "learning semantic representations for novel words leveraging both form and context"
  Abstract: Word embeddings are a key component of high-performing natural language processing (NLP) systems, but it remains a challenge to learn good representations for novel words on the fly, i.e., for words that did not occur in the training data. The general problem setting is that word embeddings are induced on an unlabeled training corpus and then a model is trained that embeds novel words into this induced embedding space. Currently, two approaches for learning embeddings of novel words exist: (i) learning an embedding from the novel word's surface-form (e.g., subword n-grams) and (ii) learning an embedding from the context in which it occurs. In this paper, we propose an architecture that leverages both sources of information - surface-form and context - and show that it results in large increases in embedding quality. Our architecture obtains state-of-the-art results on the Definitional Nonce and Contextual Rare Words datasets. As input, we only require an embedding set and an unlabeled corpus for training our architecture to produce embeddings appropriate for the induced embedding space. Thus, our model can easily be integrated into any existing NLP system and enhance its capability to handle novel words.

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"
    - "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"
    - "attention is all you need"

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"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "learning task specific representation for novel words in sequence labeling"):
  with members:
    - "chinese ner using lattice lstm"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mimicking word embeddings using subword rnns"
    - "end to end learning for self driving cars"
    - "towards cross lingual distributed representations without parallel text trained with adversarial autoencoders"
    - "finding function in form compositional character models for open vocabulary word representation"

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"
    - "attention is all you need"
    - "mimicking word embeddings using subword rnns"
    - "high risk learning acquiring new word vectors from tiny data"
    - "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 "attending form and context to generate specialized out of vocabularywords representations"):
  with members:
    - "morphological word embeddings"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "mimicking word embeddings using subword rnns"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)