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: "allennlp a deep semantic natural language processing platform"
  Abstract: This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) high-level abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy. It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)). AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence.

Connected through hyperedge HE-1 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "assessing the benchmarking capacity of machine reading comprehension datasets"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "adversarial nli a new benchmark for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multi task deep neural networks for natural language understanding"
    - "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "freelb enhanced adversarial training for language understanding"

Connected through hyperedge HE-3 (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-4 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-5 (induced by source paper "jiant a software toolkit for research on general purpose text understanding models"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "albert a lite bert for self supervised learning of language representations"
    - "probing what different nlp tasks teach machines about function word comprehension"

Connected through hyperedge HE-6 (induced by source paper "interpretable time budget constrained contextualization for re ranking"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "passage re ranking with bert"
    - "a study on the interpretability of neural retrieval models using deepshap"
    - "document expansion by query prediction"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "an updated duet model for passage re ranking"
    - "let s measure run time extending the ir replicability infrastructure to include performance aspects"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-7 (induced by source paper "cotk an open source toolkit for fast development and fair evaluation of text generation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "challenges in building intelligent open domain dialog systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-8 (induced by source paper "understanding the downstream instability of word embeddings"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "the influence of down sampling strategies on svd word embedding stability"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "on the dimensionality of word embedding"
    - "attention is all you need"
    - "compressing word embeddings via deep compositional code learning"

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)