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: "trimming and improving skip thought vectors"
  Abstract: The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to trim and improve it. First, we validate a hypothesis that, given a current sentence, inferring the previous and inferring the next sentence provide similar supervision power, therefore only one decoder for predicting the next sentence is preserved in our trimmed skip-thought model. Second, we present a connection layer between encoder and decoder to help the model to generalize better on semantic relatedness tasks. Third, we found that a good word embedding initialization is also essential for learning better sentence representations. We train our model unsupervised on a large corpus with contiguous sentences, and then evaluate the trained model on 7 supervised tasks, which includes semantic relatedness, paraphrase detection, and text classification benchmarks. We empirically show that, our proposed model is a faster, lighter-weight and equally powerful alternative to the original skip-thought model.

Connected through hyperedge HE-1 (induced by source paper "an end to end neural network framework for text clustering"):
  with members:
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "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-2 (induced by source paper "what you can cram into a single vector probing sentence embeddings for linguistic properties"):
  with members:
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep image prior"
    - "advances in pre training distributed word representations"
    - "convolutional sequence to sequence learning"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-3 (induced by source paper "embedding text in hyperbolic spaces"):
  with members:
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "specialising word vectors for lexical entailment"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "speeding up context based sentence representation learning with non autoregressive convolutional decoding"
    - "hyperlex a large scale evaluation of graded lexical entailment"

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)