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: "towards a human like open domain chatbot"
  Abstract: We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

Connected through hyperedge HE-2 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-3 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "monet unsupervised scene decomposition and representation"

Connected through hyperedge HE-4 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation"):
  with members:
    - "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"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-5 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "mass masked sequence to sequence pre training for language generation"
    - "sequence embeddings help to identify fraudulent cases in healthcare insurance"
    - "soft edit distance for differentiable comparison of symbolic sequences"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"

Connected through hyperedge HE-6 (induced by source paper "on the discrepancy between density estimation and sequence generation"):
  with members:
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "integer discrete flows and lossless compression"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "mixture models for diverse machine translation tricks of the trade"
    - "discrete flows invertible generative models of discrete data"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "arabert transformer based model for arabic language understanding"):
  with members:
    - "neural arabic question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "albert a lite bert for self supervised learning of language representations"
    - "arsentd lev a multi topic corpus for target based sentiment analysis in arabic levantine tweets"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (induced by source paper "few shot natural language generation for task oriented dialog"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "plug and play language models a simple approach to controlled text generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "defending against neural fake news"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "dialogpt large scale generative pre training for conversational response generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

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)