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: "multi step regression learning for compositional distributional semantics"
  Abstract: We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors and arguments as vectors. We introduce a new learning method for tensors, generalising the approach of Baroni and Zamparelli (2010). We evaluate it on two benchmark data sets, and find it to outperform existing leading methods. We argue in our analysis that the nature of this learning method also renders it suitable for solving more subtle problems compositional distributional models might face.

Connected through hyperedge HE-1 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator"):
  with members:
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "measuring compositionality in representation learning"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "analogs of linguistic structure in deep representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-3 (induced by source paper "a typedriven vector semantics for ellipsis with anaphora using lambek calculus with limited contraction"):
  with members:
    - "classical copying versus quantum entanglement in natural language the case of vp ellipsis"
    - "a generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras"
    - "quantifier scope in categorical compositional distributional semantics"
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "coordination in categorical compositional distributional semantics"
    - "evaluating neural word representations in tensor based compositional settings"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "modeling semantic compositionality with sememe knowledge"):
  with members:
    - "cos960 a chinese word similarity dataset of 960 word pairs"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "improving sparse word representations with distributional inference for semantic composition"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-5 (induced by source paper "don t blame distributional semantics if it can t do entailment"):
  with members:
    - "deep contextualized word representations"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-6 (induced by source paper "compositionality for recursive neural networks"):
  with members:
    - "interacting conceptual spaces i grammatical composition of concepts"
    - "recursive neural networks can learn logical semantics"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "distributed representations of words and phrases and their compositionality"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-7 (induced by source paper "learning semantic sentence embeddings using sequential pair wise discriminator"):
  with members:
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-8 (induced by source paper "paraphrase supervised models of compositionality"):
  with members:
    - "distributed representations of sentences and documents"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "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)