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: "cutting recursive autoencoder trees"
  Abstract: Deep Learning models enjoy considerable success in Natural Language Processing. While deep architectures produce useful representations that lead to improvements in various tasks, they are often difficult to interpret. This makes the analysis of learned structures particularly difficult. In this paper, we rely on empirical tests to see whether a particular structure makes sense. We present an analysis of the Semi-Supervised Recursive Autoencoder, a well-known model that produces structural representations of text. We show that for certain tasks, the structure of the autoencoder can be significantly reduced without loss of classification accuracy and we evaluate the produced structures using human judgment.

Connected through hyperedge HE-1 (induced by source paper "exploiting deep learning for persian sentiment analysis"):
  with members:
    - "a hybrid convolutional variational autoencoder for text generation"
    - "fasttext zip compressing text classification models"
    - "semi supervised sequence learning"
    - "semisupervised autoencoder for sentiment analysis"
    - "convolutional neural networks for sentence classification"
    - "an autoencoder approach to learning bilingual word representations"
    - "ensemble of generative and discriminative techniques for sentiment analysis of movie reviews"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "distributed representations for compositional semantics"):
  with members:
    - "learning multilingual word representations using a bag of words autoencoder"
    - "multilingual models for compositional distributed semantics"
    - "a convolutional neural network for modelling sentences"
    - "category theoretic quantitative compositional distributional models of natural language semantics"
    - "polyglot distributed word representations for multilingual nlp"
    - "efficient estimation of word representations in vector space"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-3 (induced by source paper "not not bad is not bad a distributional account of negation"):
  with members:
    - "multi step regression learning for compositional distributional semantics"
    - "domain and function a dual space model of semantic relations and compositions"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-4 (induced by source paper "recurrent convolutional neural networks for discourse compositionality"):
  with members:
    - "concrete sentence spaces for compositional distributional models of meaning"
    - "from frequency to meaning vector space models of semantics"

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)