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: "dynamic compositional neural networks over tree structure"
  Abstract: Tree-structured neural networks have proven to be effective in learning semantic representations by exploiting syntactic information. In spite of their success, most existing models suffer from the underfitting problem: they recursively use the same shared compositional function throughout the whole compositional process and lack expressive power due to inability to capture the richness of compositionality. In this paper, we address this issue by introducing the dynamic compositional neural networks over tree structure (DC-TreeNN), in which the compositional function is dynamically generated by a meta network. The role of meta-network is to capture the metaknowledge across the different compositional rules and formulate them. Experimental results on two typical tasks show the effectiveness of the proposed models.

Connected through hyperedge HE-1 (induced by source paper "an enhanced tree lstm architecture for sentence semantic modeling using typed dependencies"):
  with members:
    - "a deep network model for paraphrase detection in short text messages"
    - "recurrent neural network based semantic variational autoencoder for sequence to sequence learning"
    - "tag enhanced tree structured neural networks for implicit discourse relation classification"
    - "dynamic compositionality in recursive neural networks with structure aware tag representations"
    - "learning to compose words into sentences with reinforcement learning"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "vector of locally aggregated word embeddings vlawe a novel document level representation"):
  with members:
    - "automated essay scoring with string kernels and word embeddings"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing"
    - "from image to text classification a novel approach based on clustering word embeddings"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-4 (induced by source paper "an attention gated convolutional neural network for sentence classification"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "word embedding perturbation for sentence classification"
    - "self normalizing neural networks"
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "dynamic compositionality in recursive neural networks with structure aware tag representations"):
  with members:
    - "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"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "learned in translation contextualized word vectors"
    - "decoupled weight decay regularization"
    - "shortcut stacked sentence encoders for multi domain inference"

Connected through hyperedge HE-6 (induced by source paper "learning to compose over tree structures via pos tags"):
  with members:
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"
    - "learning feed forward one shot learners"
    - "recurrent dropout without memory loss"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "neural semantic encoders"
    - "theano a python framework for fast computation of mathematical expressions"
    - "self adaptive hierarchical sentence model"

Connected through hyperedge HE-7 (induced by source paper "adaptive convolutional filter generation for natural language understanding"):
  with members:
    - "multi view sentence representation learning"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "learning loss functions for semi supervised learning via discriminative adversarial networks"
    - "convolutional sequence to 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)