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: "towards neural machine translation with latent tree attention"
  Abstract: Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a recurrent neural network grammar encoder with a novel attentional RNNG decoder and applying policy gradient reinforcement learning to induce unsupervised tree structures on both the source and target. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.

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 "inducing constituency trees through neural machine translation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "compound probabilistic context free grammars for grammar induction"
    - "an imitation learning approach to unsupervised parsing"
    - "inducing grammars with and for neural machine translation"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "linguistically informed self attention for semantic role labeling"
    - "grammar induction with neural language models an unusual replication"
    - "learning to parse and translate improves neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "inducing grammars with and for neural machine translation"):
  with members:
    - "structured attention networks"
    - "learning to parse and translate improves neural machine translation"
    - "learning structured text representations"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "neural machine translation with source side latent graph parsing"
    - "stronger baselines for trustable results in neural machine translation"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "tree to tree neural networks for program translation"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "towards string to tree neural machine translation"
    - "a survey of machine learning for big code and naturalness"
    - "towards synthesizing complex programs from input output examples"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "latent attention for if then program synthesis"

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)