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: "linguistic generalization and compositionality in modern artificial neural networks"
  Abstract: In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must posses...

Connected through hyperedge HE-1 (induced by source paper "capacity bandwidth and compositionality in emergent language learning"):
  with members:
    - "on the interaction between supervision and self play in emergent communication"
    - "anti efficient encoding in emergent communication"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "countering language drift via visual grounding"
    - "compositional generalization through meta sequence to sequence learning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "is the red square big malevic modeling adjectives leveraging visual contexts"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "how clever is the film model and how clever can it be"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"
    - "systematic generalization what is required and can it be learned"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "pragmatically informative image captioning with character level reference"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-3 (induced by source paper "probing the phonetic and phonological knowledge of tones in mandarin tts models"):
  with members:
    - "using priming to uncover the organization of syntactic representations in neural language models"
    - "fastspeech fast robust and controllable text to speech"
    - "towards transfer learning for end to end speech synthesis from deep pre trained language models"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "representation learning for discovering phonemic tone contours"
    - "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"
    - "pre training with whole word masking for chinese bert"
    - "waveglow a flow based generative network for speech synthesis"

Connected through hyperedge HE-4 (induced by source paper "composing and embedding the words as classifiers model of grounded semantics"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "semantic composition via probabilistic model theory"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "going deeper with convolutions"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "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)