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: "siamese cbow optimizing word embeddings for sentence representations"
  Abstract: We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sentence representation, and, thus, likely to be suboptimal. Siamese CBOW handles this problem by training word embeddings directly for the purpose of being averaged. The underlying neural network learns word embeddings by predicting, from a sentence representation, its surrounding sentences. We show the robustness of the Siamese CBOW model by evaluating it on 20 datasets stemming from a wide variety of sources.

Connected through hyperedge HE-1 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "pre train interact fine tune a novel interaction representation for text classification"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"
    - "regularizing and optimizing lstm language models"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "sentence transition matrix an efficient approach that preserves sentence semantics"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "unsupervised cross lingual transfer of word embedding spaces"
    - "paraphrase thought sentence embedding module imitating human language recognition"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-4 (induced by source paper "compositional network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "relational inductive biases deep learning and graph networks"
    - "graph embedding techniques applications and performance a survey"
    - "an attention based collaboration framework for multi view network representation learning"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "multimodal emotion classification"):
  with members:
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "which emoji talks best for my picture"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "fasttext zip compressing text classification models"
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"

Connected through hyperedge HE-6 (induced by source paper "a dynamic evolutionary framework for timeline generation based on distributed representations"):
  with members:
    - "growing story forest online from massive breaking news"
    - "socially informed timeline generation for complex events"
    - "enriching word vectors with subword information"
    - "timemachine timeline generation for knowledge base entities"
    - "timeline generation tracking individuals on twitter"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "an end to end neural network framework for text clustering"):
  with members:
    - "trimming and improving skip thought vectors"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "neural networks for information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "attentive memory networks efficient machine reading for conversational search"
    - "neural ranking models with weak supervision"
    - "reply with proactive recommendation of email attachments"
    - "a neural knowledge language model"
    - "learning to attend copy and generate for session based query suggestion"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"

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)