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: "neural question answering at bioasq 5b"
  Abstract: This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.

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"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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 "adversarial domain adaptation for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "adversarial domain adaptation for duplicate question detection"
    - "multi task learning with sample re weighting for machine reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "pre trained language model for biomedical question answering"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "using neural networks for relation extraction from biomedical literature"
    - "publicly available clinical bert embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-4 (induced by source paper "a question entailment approach to question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cmu livemedqa at trec 2017 liveqa a consumer health question answering system"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"

Connected through hyperedge HE-5 (induced by source paper "how to pre train your model comparison of different pre training models for biomedical question answering"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "quasar datasets for question answering by search and reading"
    - "neural domain adaptation for biomedical question answering"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-6 (induced by source paper "constructing datasets for multi hop reading comprehension across documents"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "end to end differentiable proving"
    - "zero shot relation extraction via reading comprehension"
    - "making neural qa as simple as possible but not simpler"
    - "ask the right questions active question reformulation with reinforcement 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)