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: "texar a modularized versatile and extensible toolkit for text generation"
  Abstract: We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled and plugged in/swapped out. The toolkit also supports a rich set of large-scale pretrained models. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.

Connected through hyperedge HE-1 (induced by source paper "dynamic knowledge routing network for target guided open domain conversation"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "end to end knowledge routed relational dialogue system for automatic diagnosis"
    - "target guided open domain conversation"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "towards deep conversational recommendations"
    - "emergent communication through negotiation"
    - "conversational ai the science behind the alexa prize"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-2 (induced by source paper "target guided open domain conversation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "unsupervised text style transfer using language models as discriminators"
    - "towards deep conversational recommendations"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "emergent communication through negotiation"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive text style transfer"):
  with members:
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"

Connected through hyperedge HE-4 (induced by source paper "word2ket space efficient word embeddings inspired by quantum entanglement"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "low memory neural network training a technical report"
    - "generating long sequences with sparse transformers"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "improving sequence to sequence learning via optimal transport"
    - "roberta a robustly optimized bert pretraining approach"
    - "the state of sparsity in deep neural networks"
    - "high accuracy low precision training"

Connected through hyperedge HE-5 (induced by source paper "leafnats an open source toolkit and live demo system for neural abstractive text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "deep reinforcement learning for sequence to sequence models"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "xnmt the extensible neural machine translation toolkit"
    - "bottom up abstractive summarization"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "dialogpt large scale generative pre training for conversational response generation"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "dlgnet a transformer based model for dialogue response generation"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "consistent dialogue generation with self supervised feature learning"
    - "structuring latent spaces for stylized response generation"

Connected through hyperedge HE-7 (induced by source paper "text infilling"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "unsupervised text style transfer using language models as discriminators"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generative image inpainting with contextual attention"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-8 (induced by source paper "counterfactual story reasoning and generation"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "bertscore evaluating text generation with bert"
    - "probing neural network comprehension of natural language arguments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

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)