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: "a continuously growing dataset of sentential paraphrases"
  Abstract: A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ~70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.

Connected through hyperedge HE-1 (induced by source paper "calibration of pre trained transformers"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "calibration of encoder decoder models for neural machine translation"
    - "simplified neural unsupervised domain adaptation"
    - "evaluating lottery tickets under distributional shifts"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "revealing the dark secrets of bert"
    - "how to fine tune bert for text classification"

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 "generating black box adversarial examples for text classifiers using a deep reinforced model"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "gray box adversarial training"
    - "adversarial texts with gradient methods"
    - "motivating the rules of the game for adversarial example research"

Connected through hyperedge HE-4 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "quaternion knowledge graph embedding"
    - "attending to mathematical language with transformers"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "paws paraphrase adversaries from word scrambling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "assessing composition in sentence vector representations"
    - "gender bias in coreference resolution"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "open subtitles paraphrase corpus for six languages"

Connected through hyperedge HE-6 (induced by source paper "an empirical comparison on imitation learning and reinforcement learning for paraphrase generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "learning semantic sentence embeddings using pair wise discriminator"
    - "the natural language decathlon multitask learning as question answering"
    - "a study of reinforcement learning for neural machine translation"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-7 (induced by source paper "simple and effective paraphrastic similarity from parallel translations"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "transfer fine tuning a bert case study"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "syntactic scaffolds for semantic structures"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"

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)