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 generating long and coherent text with multi level latent variable models"
  Abstract: Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several multi-level structures to learn a VAE model to generate long, and coherent text. In particular, we use a hierarchy of stochastic layers between the encoder and decoder networks to generate more informative latent codes. We also investigate a multi-level decoder structure to learn a coherent long-term structure by generating intermediate sentence representations as high-level plan vectors. Empirical results demonstrate that a multi-level VAE model produces more coherent and less repetitive long text compared to the standard VAE models and can further mitigate the posterior-collapse issue.

Connected through hyperedge HE-1 (induced by source paper "a unified system for aggression identification in english code mixed and uni lingual texts"):
  with members:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "mattnet modular attention network for referring expression comprehension"
    - "universal language model fine tuning for text classification"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification"):
  with members:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "mattnet modular attention network for referring expression comprehension"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"
    - "advances in pre training distributed word representations"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-3 (induced by source paper "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "text generation with exemplar based adaptive decoding"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-4 (induced by source paper "inset sentence infilling with inter sentential generative pre training"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "the curious case of neural text degeneration"
    - "what makes a good story designing composite rewards for visual storytelling"
    - "spanbert improving pre training by representing and predicting spans"
    - "abductive reasoning as self supervision for common sense question answering"
    - "sentence bert sentence embeddings using siamese bert networks"

Connected through hyperedge HE-5 (induced by source paper "long and diverse text generation with planning based hierarchical variational model"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "step by step separating planning from realization in neural data to text generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "texygen a benchmarking platform for text generation models"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "order planning neural text generation from structured data"

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)