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: "long and diverse text generation with planning based hierarchical variational model"
  Abstract: Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.

Connected through hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"
    - "strategies for structuring story generation"

Connected through hyperedge HE-2 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue"):
  with members:
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "visualizing and understanding the effectiveness of bert"
    - "learning to select knowledge for response generation in dialog systems"

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)