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 question entailment approach to question answering"
  Abstract: Background#R##N#One of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is mapping new questions to formerly answered questions that are “similar”.

Connected through hyperedge HE-1 (induced by source paper "learning contextualized document representations for healthcare answer retrieval"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning dense representations for entity retrieval"
    - "pubmedqa a dataset for biomedical research question answering"
    - "zero shot entity linking by reading entity descriptions"
    - "sector a neural model for coherent topic segmentation and classification"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "end to end retrieval in continuous space"

Connected through hyperedge HE-2 (induced by source paper "generating scientific question answering corpora from q a forums"):
  with members:
    - "emrqa a large corpus for question answering on electronic medical records"
    - "biomedical question answering via weighted neural network passage retrieval"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-3 (induced by source paper "pentagon at mediqa 2019 multi task learning for filtering and re ranking answers using language inference and question entailment"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "transforming question answering datasets into natural language inference datasets"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a survey on multi task learning"
    - "neural models for information retrieval"

Connected through hyperedge HE-4 (induced by source paper "doubletransfer at mediqa 2019 multi source transfer learning for natural language understanding in the medical domain"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "multi task learning with sample re weighting for machine reading comprehension"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "dr quad at mediqa 2019 towards textual inference and question entailment using contextualized representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "an overview of multi task learning in deep neural networks"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-6 (induced by source paper "iitp at mediqa 2019 systems report for natural language inference question entailment and question answering"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)