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: "lessons from natural language inference in the clinical domain"
  Abstract: State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.

Connected through hyperedge HE-1 (induced by source paper "uncc biomedical semantic question answering systems bioasq task 7b phase b"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "publicly available clinical bert embeddings"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural question answering at bioasq 5b"
    - "squad 100 000 questions for machine comprehension of text"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "probing biomedical embeddings from language models"
    - "ml net multi label classification of biomedical texts with deep neural networks"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-3 (induced by source paper "probing biomedical embeddings from language models"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"
    - "deep rnns encode soft hierarchical syntax"
    - "dissecting contextual word embeddings architecture and representation"

Connected through hyperedge HE-4 (induced by source paper "surf at mediqa 2019 improving performance of natural language inference in the clinical domain by adopting pre trained language model"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "dr quad at mediqa 2019 towards textual inference and question entailment using contextualized representations"
    - "doubletransfer at mediqa 2019 multi source transfer learning for natural language understanding in the medical domain"
    - "multi task deep neural networks for natural language understanding"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-5 (induced by source paper "uw bhi at mediqa 2019 an analysis of representation methods for medical natural language inference"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "syntactic scaffolds for semantic structures"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic parsing for task oriented dialog using hierarchical representations"

Connected through hyperedge HE-6 (induced by source paper "towards automatic generation of shareable synthetic clinical notes using neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep ehr chronic disease prediction using medical notes"
    - "towards the creation of a large corpus of synthetically identified clinical notes"
    - "fine tuned language models for text classification"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "towards measuring membership privacy"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "de identification of patient notes with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "revealing the dark secrets of bert"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "assessing bert s syntactic abilities"
    - "pay less attention with lightweight and dynamic convolutions"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-8 (induced by source paper "learning conceptual contexual embeddings for medical text"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "ernie enhanced language representation with informative entities"
    - "beyond word embeddings learning entity and concept representations from large scale knowledge bases"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "a comprehensive survey of graph embedding problems techniques and applications"

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)