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: "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
  Abstract: Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings). To this end, we propose a novel latent-variable generative model for semi-supervised syntactic dependency parsing. As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters. Our method (Differentiable Perturb-and-Parse) relies on differentiable dynamic programming over stochastically perturbed edge scores. We demonstrate effectiveness of our approach with experiments on English, French and Swedish.

Connected through hyperedge HE-1 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "sparse sequence to sequence models"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "structured attention networks"
    - "learning structured text representations"

Connected through hyperedge HE-2 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-3 (induced by source paper "effective estimation of deep generative language models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "preventing posterior collapse with delta vaes"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "neural autoregressive flows"
    - "a hierarchical latent structure for variational conversation modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "amr parsing as graph prediction with latent alignment"

Connected through hyperedge HE-4 (induced by source paper "cooperative learning of disjoint syntax and semantics"):
  with members:
    - "on tree based neural sentence modeling"
    - "listops a diagnostic dataset for latent tree learning"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"
    - "deep contextualized word representations"
    - "learning to generate reviews and discovering sentiment"
    - "automatically composing representation transformations as a means for generalization"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"

Connected through hyperedge HE-5 (induced by source paper "learning latent trees with stochastic perturbations and differentiable dynamic programming"):
  with members:
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "towards dynamic computation graphs via sparse latent structure"
    - "a tree based decoder for neural machine translation"
    - "grammar induction with neural language models an unusual replication"
    - "structured attention networks"
    - "learning structured text representations"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "semi supervisedly co embedding attributed networks"):
  with members:
    - "semi supervised learning on graphs with generative adversarial nets"
    - "variational autoencoders for semi supervised text classification"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-7 (induced by source paper "direct optimization through arg max for discrete variational auto encoder"):
  with members:
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "toward controlled generation of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "learning hard alignments with variational inference"
    - "emergence of grounded compositional language in multi agent populations"

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)