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: "can machine generate traditional chinese poetry a feigenbaum test"
  Abstract: Recent progress in neural learning demonstrated that machines can do well in regularized tasks, e.g., the game of Go. However, artistic activities such as poem generation are still widely regarded as human's special capability. In this paper, we demonstrate that a simple neural model can imitate human in some tasks of art generation. We particularly focus on traditional Chinese poetry, and show that machines can do as well as many contemporary poets and weakly pass the Feigenbaum Test, a variant of Turing test in professional domains. Our method is based on an attention-based recurrent neural network, which accepts a set of keywords as the theme and generates poems by looking at each keyword during the generation. A number of techniques are proposed to improve the model, including character vector initialization, attention to input and hybrid-style training. Compared to existing poetry generation methods, our model can generate much more theme-consistent and semantic-rich poems.

Connected through hyperedge HE-1 (induced by source paper "an iterative polishing framework based on quality aware masked language model for chinese poetry generation"):
  with members:
    - "pretraining based natural language generation for text summarization"
    - "chinese poetry generation with a salient clue mechanism"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "chinese poetry generation with a working memory model"
    - "flexible and creative chinese poetry generation using neural memory"
    - "chinese poetry generation with planning based neural network"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-2 (induced by source paper "a multi modal chinese poetry generation model"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "chinese song iambics generation with neural attention based model"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "hierarchical question image co attention for visual question answering"
    - "generating chinese classical poems with rnn encoder decoder"
    - "chinese poetry generation with planning based neural network"
    - "a neural attention model for abstractive sentence summarization"
    - "backward and forward language modeling for constrained sentence generation"

Connected through hyperedge HE-3 (induced by source paper "chinese poetry generation with flexible styles"):
  with members:
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "chinese song iambics generation with neural attention based model"
    - "chinese poetry generation with planning based neural network"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "flexible and creative chinese poetry generation using neural memory"):
  with members:
    - "chinese song iambics generation with neural attention based model"
    - "chinese poetry generation with planning based neural network"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"

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)