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: "aligning packed dependency trees a theory of composition for distributional semantics"
  Abstract: We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees. We show that these structures have the potential to capture the full sentential contexts of a lexeme and provide a uniform basis for the composition of distributional knowledge in a way that captures both mutual disambiguation and generalization.

Connected through hyperedge HE-1 (induced by source paper "temporal and aspectual entailment"):
  with members:
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "one representation per word does it make sense for composition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (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"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-3 (induced by source paper "the lifted matrix space model for semantic composition"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "natural language inference over interaction space"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "neural semantic encoders"
    - "long short term memory over tree structures"
    - "compositional distributional semantics with long short term memory"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "one representation per word does it make sense for composition"):
  with members:
    - "the mechanism of additive composition"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "improving sparse word representations with distributional inference for semantic composition"
    - "from paraphrase database to compositional paraphrase model and back"
    - "learning to understand phrases by embedding the dictionary"
    - "do multi sense embeddings improve natural language understanding"
    - "a study of entanglement in a categorical framework of natural language"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "improving semantic composition with offset inference"):
  with members:
    - "learning semantically and additively compositional distributional representations"
    - "discriminative neural sentence modeling by tree based convolution"
    - "a study of entanglement in a categorical framework of natural language"
    - "distributed tree kernels"
    - "domain and function a dual space model of semantic relations and compositions"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "concrete sentence spaces for compositional distributional models of meaning"

Connected through hyperedge HE-6 (induced by source paper "improving sparse word representations with distributional inference for semantic composition"):
  with members:
    - "improved semantic representations from tree structured long short term memory networks"
    - "from paraphrase database to compositional paraphrase model and back"
    - "discriminative neural sentence modeling by tree based convolution"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of words and phrases and their compositionality"
    - "multi step regression learning for compositional distributional semantics"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

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)