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: "crf autoencoder for unsupervised dependency parsing"
  Abstract: Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. In this paper, we develop an unsupervised dependency parsing model based on the CRF autoencoder. The encoder part of our model is discriminative and globally normalized which allows us to use rich features as well as universal linguistic priors. We propose an exact algorithm for parsing as well as a tractable learning algorithm. We evaluated the performance of our model on eight multilingual treebanks and found that our model achieved comparable performance with state-of-the-art approaches.

Connected through hyperedge HE-1 (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-2 (induced by source paper "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"):
  with members:
    - "inducing grammars with and for neural machine translation"
    - "deep contextualized word representations"
    - "structured attention networks"
    - "learning structured text representations"
    - "neural probabilistic model for non projective mst parsing"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "do latent tree learning models identify meaningful structure in sentences"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of syntactic structure with invertible neural projections"):
  with members:
    - "deep contextualized word representations"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "i revnet deep invertible networks"
    - "dependency grammar induction with neural lexicalization and big training data"
    - "density estimation using real nvp"
    - "language as a latent variable discrete generative models for sentence compression"
    - "unsupervised neural hidden markov models"
    - "unsupervised pos induction with word embeddings"

Connected through hyperedge HE-4 (induced by source paper "dependency grammar induction with a neural variational transition based parser"):
  with members:
    - "global transition based non projective dependency parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "a generative parser with a discriminative recognition algorithm"
    - "recurrent neural network grammars"
    - "enriching word vectors with subword information"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "language as a latent variable discrete generative models for sentence compression"
    - "transition based dependency parsing with stack long short term memory"

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)