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: "no metrics are perfect adversarial reward learning for visual storytelling"
  Abstract: Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic eval- uation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.

Connected through hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "topic guided variational autoencoders for text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "visual storytelling via predicting anchor word embeddings in the stories"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "deep residual learning for image recognition"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-4 (induced by source paper "improving sequence to sequence learning via optimal transport"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "sequence generation with guider network"
    - "policy optimization as wasserstein gradient flows"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "improving gans using optimal transport"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-5 (induced by source paper "curiosity driven reinforcement learning for diverse visual paragraph generation"):
  with members:
    - "exploration by random network distillation"
    - "diverse and coherent paragraph generation from images"
    - "large scale study of curiosity driven learning"
    - "context aware visual policy network for sequence level image captioning"
    - "coarse to fine annotation enrichment for semantic segmentation learning"
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "improved image captioning via policy gradient optimization of spider"
    - "show adapt and tell adversarial training of cross domain image captioner"

Connected through hyperedge HE-6 (induced by source paper "generative question refinement with deep reinforcement learning in retrieval based qa system"):
  with members:
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "proximal policy optimization and its dynamic version for sequence generation"
    - "identifying well formed natural language questions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "learning to paraphrase for question answering"
    - "sentence simplification with deep reinforcement learning"
    - "ask the right questions active question reformulation with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "what makes a good story designing composite rewards for visual storytelling"):
  with members:
    - "uniter learning universal image text representations"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "plan and write towards better automatic storytelling"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "learning neural templates for text generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "hierarchical neural story generation"

Connected through hyperedge HE-8 (induced by source paper "hierarchical photo scene encoder for album storytelling"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "hierarchical boundary aware neural encoder for video captioning"

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)