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: "real time inference in multi sentence tasks with deep pretrained transformers"
  Abstract: The use of deep pretrained bidirectional transformers has led to remarkable progress in learning multi-sentence representations for downstream language understanding tasks (Devlin et al., 2018). For tasks that make pairwise comparisons, e.g. matching a given context with a corresponding response, two approaches have permeated the literature. A Cross-encoder performs full self-attention over the pair; a Bi-encoder performs self-attention for each sequence separately, and the final representation is a function of the pair. While Cross-encoders nearly always outperform Bi-encoders on various tasks, both in our work and others' (Urbanek et al., 2019), they are orders of magnitude slower, which hampers their ability to perform real-time inference. In this work, we develop a new architecture, the Poly-encoder, that is designed to approach the performance of the Cross-encoder while maintaining reasonable computation time. Additionally, we explore two pretraining schemes with different datasets to determine how these affect the performance on our chosen dialogue tasks: ConvAI2 and DSTC7 Track 1. We show that our models achieve state-of-the-art results on both tasks; that the Poly-encoder is a suitable replacement for Bi-encoders and Cross-encoders; and that even better results can be obtained by pretraining on a large dialogue dataset.

Connected through hyperedge HE-1 (induced by source paper "pre training tasks for embedding based large scale retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "reqa an evaluation for end to end answer retrieval models"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "stochastic negative mining for learning with large output spaces"
    - "training millions of personalized dialogue agents"

Connected through hyperedge HE-2 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents"):
  with members:
    - "hierarchical decision making by generating and following natural language instructions"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "countering language drift via visual grounding"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "target guided open domain conversation"
    - "learning semantic textual similarity from conversations"
    - "textworld a learning environment for text based games"

Connected through hyperedge HE-3 (induced by source paper "the second conversational intelligence challenge convai2"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "data augmentation for neural online chat response selection"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "dialogue natural language inference"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "the dialogue dodecathlon open domain knowledge and image grounded conversational agents"):
  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"
    - "lxmert learning cross modality encoder representations from transformers"
    - "the curious case of neural text degeneration"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"

Connected through hyperedge HE-5 (induced by source paper "all in one image grounded conversational agents"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "real or fake learning to discriminate machine from human generated text"
    - "12 in 1 multi task vision and language representation learning"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-6 (induced by source paper "sentence bert sentence embeddings using siamese bert networks"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "on measuring social biases in sentence encoders"
    - "bertscore evaluating text generation with bert"
    - "understanding the behaviors of bert in ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning semantic textual similarity from conversations"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"

Connected through hyperedge HE-7 (induced by source paper "aloha artificial learning of human attributes for dialogue agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "bert for coreference resolution baselines and analysis"
    - "learning to speak and act in a fantasy text adventure game"
    - "keep calm and switch on preserving sentiment and fluency in semantic text exchange"
    - "game based video context dialogue"
    - "alquist the alexa prize socialbot"

Connected through hyperedge HE-8 (induced by source paper "don t say that making inconsistent dialogue unlikely with unlikelihood training"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "hellaswag can a machine really finish your sentence"
    - "language models as knowledge bases"
    - "the curious case of neural text degeneration"
    - "latent relation language models"
    - "counterfactual story reasoning and generation"
    - "negative training for neural dialogue response generation"
    - "cooperative generator discriminator networks for abstractive summarization with narrative flow"

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)