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: "rethinking skip thought a neighborhood based approach"
  Abstract: We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model.

Connected through hyperedge HE-1 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on the convergence of adam and beyond"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "the natural language decathlon multitask learning as question answering"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "comprehensive supersense disambiguation of english prepositions and possessives"

Connected through hyperedge HE-2 (induced by source paper "cbow is not all you need combining cbow with the compositional matrix space model"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-3 (induced by source paper "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert rediscovers the classical nlp pipeline"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "no training required exploring random encoders for sentence classification"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-4 (induced by source paper "learning general purpose distributed sentence representations via large scale multi task learning"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning to generate reviews and discovering sentiment"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "deconvolutional latent variable model for text sequence matching"
    - "dissent sentence representation learning from explicit discourse relations"
    - "learned in translation contextualized word vectors"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"

Connected through hyperedge HE-5 (induced by source paper "icorating a deep learning system for scam ico identification"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "rationalizing neural predictions"
    - "understanding neural networks through representation erasure"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "visualizing and understanding neural models in nlp"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "effective approaches to attention based neural machine translation"
    - "skip thought vectors"

Connected through hyperedge HE-6 (induced by source paper "improving visually grounded sentence representations with self attention"):
  with members:
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "efficient vector representation for documents through corruption"
    - "emergent translation in multi agent communication"
    - "sound word2vec learning word representations grounded in sounds"
    - "deconvolutional paragraph representation learning"
    - "a structured self attentive sentence embedding"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-7 (induced by source paper "trimming and improving skip thought vectors"):
  with members:
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "semi supervised sequence learning"
    - "a large annotated corpus for learning natural language inference"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "adam a method for stochastic optimization"

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)