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: "the lambada dataset word prediction requiring a broad discourse context"
  Abstract: We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-of-the-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "adversarial nli a new benchmark for natural language understanding"):
  with members:
    - "adversarial filters of dataset biases"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "abductive commonsense reasoning"
    - "codah an adversarially authored question answer dataset for common sense"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "multi task deep neural networks for natural language understanding"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"

Connected through hyperedge HE-3 (induced by source paper "compressive transformers for long range sequence modelling"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "the curious case of neural text degeneration"
    - "dynamic evaluation of transformer language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "large memory layers with product keys"

Connected through hyperedge HE-4 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-5 (induced by source paper "megatron lm training multi billion parameter language models using model parallelism"):
  with members:
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dynamic evaluation of transformer language models"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-6 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-7 (induced by source paper "universal transformers"):
  with members:
    - "hyperbolic attention networks"
    - "weighted transformer network for machine translation"
    - "neural models for reasoning over multiple mentions using coreference"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "linguistic knowledge as memory for recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "attending to entities for better text understanding"):
  with members:
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "entity tracking improves cloze style reading comprehension"

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)