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: "compositional distributional semantics with long short term memory"
  Abstract: We are proposing an extension of the recursive neural network that makes use of a variant of the long short-term memory architecture. The extension allows information low in parse trees to be stored in a memory register (the `memory cell') and used much later higher up in the parse tree. This provides a solution to the vanishing gradient problem and allows the network to capture long range dependencies. Experimental results show that our composition outperformed the traditional neural-network composition on the Stanford Sentiment Treebank.

Connected through hyperedge HE-1 (induced by source paper "structure tree lstm structure aware attentional document encoders"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "tree to sequence attentional neural machine translation"
    - "neural document embeddings for intensive care patient mortality prediction"
    - "long short term memory networks for machine reading"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "interpretable structure aware document encoders with hierarchical attention"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "tree to sequence attentional neural machine translation"
    - "neural document embeddings for intensive care patient mortality prediction"
    - "long short term memory networks for machine reading"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "embedding symbolic knowledge into deep networks"):
  with members:
    - "learning logistic circuits"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-4 (induced by source paper "latent variable sentiment grammar"):
  with members:
    - "deep contextualized word representations"
    - "gaussian mixture latent vector grammars"
    - "learned in translation contextualized word vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "when are tree structures necessary for deep learning of representations"
    - "adam a method for stochastic optimization"
    - "a statistical parsing framework for sentiment classification"

Connected through hyperedge HE-5 (induced by source paper "cell aware stacked lstms for modeling sentences"):
  with members:
    - "on tree based neural sentence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "enhancing sentence embedding with generalized pooling"
    - "learning to generate reviews and discovering sentiment"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning with dynamic computation graphs"

Connected through hyperedge HE-6 (induced by source paper "a sequential neural encoder with latent structured description for modeling sentences"):
  with members:
    - "tensor fusion network for multimodal sentiment analysis"
    - "bilateral multi perspective matching for natural language sentences"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural architectures for named entity recognition"
    - "neural tree indexers for text understanding"

Connected through hyperedge HE-7 (induced by source paper "can neural networks understand logical entailment"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "learning continuous semantic representations of symbolic expressions"
    - "squad 100 000 questions for machine comprehension of text"
    - "latent predictor networks for code generation"
    - "learning to navigate in complex environments"
    - "neuro symbolic program synthesis"
    - "better computer go player with neural network and long term prediction"

Connected through hyperedge HE-8 (induced by source paper "global context neural machine translation through target side attentive residual connections"):
  with members:
    - "recurrent additive networks"
    - "predicting target language ccg supertags improves neural machine translation"
    - "learning structured text representations"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "towards string to tree neural machine translation"
    - "rra recurrent residual attention for sequence learning"

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)