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: "albert a lite bert for self supervised learning of language representations"
  Abstract: Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations, longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.The code and the pretrained models are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (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-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-4 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "contrastive multiview coding"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "momentum contrast for unsupervised visual representation learning"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-5 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models"):
  with members:
    - "are sixteen heads really better than one"
    - "language models as knowledge bases"
    - "pitfalls in the evaluation of sentence embeddings"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-6 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "defending against neural fake news"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-7 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"

Connected through hyperedge HE-8 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification"):
  with members:
    - "relational word embeddings"
    - "kg bert bert for knowledge graph completion"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "k bert enabling language representation with knowledge graph"

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)