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: "learning distributed representations of sentences from unlabelled data"
  Abstract: Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.

Connected through hyperedge HE-1 (induced by source paper "contextual lensing of universal sentence representations"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ccmatrix mining billions of high quality parallel sentences on the web"
    - "parameter efficient transfer learning for nlp"
    - "unsupervised cross lingual representation learning at scale"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"

Connected through hyperedge HE-2 (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"
    - "albert a lite bert for self supervised learning of language representations"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"

Connected through hyperedge HE-3 (induced by source paper "twinbert distilling knowledge to twin structured bert models for efficient retrieval"):
  with members:
    - "well read students learn better on the importance of pre training compact models"
    - "multi task deep neural networks for natural language understanding"
    - "model compression with multi task knowledge distillation for web scale question answering system"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "distilling task specific knowledge from bert into simple neural networks"

Connected through hyperedge HE-4 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-5 (induced by source paper "efficient sentence embedding via semantic subspace analysis"):
  with members:
    - "sbert wk a sentence embedding method by dissecting bert based word models"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vector of locally aggregated word embeddings vlawe a novel document level representation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-6 (induced by source paper "bi decoder augmented network for neural machine translation"):
  with members:
    - "reinforced dynamic reasoning for conversational question generation"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "macnet transferring knowledge from machine comprehension to sequence to sequence models"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-7 (induced by source paper "albert a lite bert for self supervised learning of language representations"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "spanbert improving pre training by representing and predicting spans"
    - "well read students learn better on the importance of pre training compact models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "structbert incorporating language structures into pre training for deep language understanding"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-8 (induced by source paper "superglue a stickier benchmark for general purpose language understanding systems"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "paws paraphrase adversaries from word scrambling"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"

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)