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: "generative neural machine for tree structures"
  Abstract: Tree structures are commonly used in the tasks of semantic analysis and understanding over the data of different modalities, such as natural language, 2D or 3D graphics and images, or Web pages. Previous studies model the tree structures in a bottom-up manner, where the leaf nodes (given in advance) are merged into internal nodes until they reach the root node. However, these models are not applicable when the leaf nodes are not explicitly specified ahead of prediction. Here, we introduce a neural machine for top-down generation of tree structures that aims to infer such tree structures without the specified leaf nodes. In this model, the history memories from ancestors are fed to a node to generate its (ordered) children in a recursive manner. This model can be utilized as a tree-structured decoder in the framework of "X to tree" learning, where X stands for any structure (e.g. chain, tree etc.) that can be represented as a latent vector. By transforming the dialogue generation problem into a sequence-to-tree task, we demonstrate the proposed X2Tree framework achieves a 11.15% increase of response acceptance ratio over the baseline methods.

Connected through hyperedge HE-1 (induced by source paper "learning by stochastic serializations"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "relational inductive biases deep learning and graph networks"
    - "recurrent estimation of distributions"
    - "neural message passing for quantum chemistry"
    - "language to logical form with neural attention"
    - "neural autoregressive distribution estimation"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "ordered neurons integrating tree structures into recurrent neural networks"):
  with members:
    - "the emergence of number and syntax units in lstm language models"
    - "on tree based neural sentence modeling"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "grammar induction with neural language models an unusual replication"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"

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.AI(Artificial Intelligence)