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: "hierarchical neural story generation"
  Abstract: We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.

Connected through hyperedge HE-1 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation"):
  with members:
    - "towards a human like open domain chatbot"
    - "a neural topical expansion framework for unstructured persona oriented dialogue generation"
    - "empgan multi resolution interactive empathetic dialogue generation"
    - "a pre training based personalized dialogue generation model with persona sparse data"
    - "fine grained sentence functions for short text conversation"
    - "the curious case of neural text degeneration"
    - "do massively pretrained language models make better storytellers"
    - "a discrete cvae for response generation on short text conversation"

Connected through hyperedge HE-2 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "the evolved transformer"

Connected through hyperedge HE-3 (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"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "analysis of diversity accuracy tradeoff in image captioning"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "the curious case of neural text degeneration"
    - "mixture models for diverse machine translation tricks of the trade"
    - "describing like humans on diversity in image captioning"
    - "learning to write with cooperative discriminators"
    - "adversarial inference for multi sentence video description"
    - "attention is all you need"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-5 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "correcting length bias in neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-6 (induced by source paper "learning to compare for better training and evaluation of open domain natural language generation models"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "bertscore evaluating text generation with bert"
    - "judge the judges a large scale evaluation study of neural language models for online review generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-7 (induced by source paper "citation text generation"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "bertscore evaluating text generation with bert"
    - "defending against neural fake news"
    - "gorc a large contextual citation graph of academic papers"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "content based citation recommendation"

Connected through hyperedge HE-8 (induced by source paper "document ranking with a pretrained sequence to sequence model"):
  with members:
    - "scaling laws for neural language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi stage document ranking with bert"
    - "passage re ranking with bert"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "show your work improved reporting of experimental results"
    - "zero shot text classification with generative 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.CL(Computation and Language)