human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "knowledge guided named entity recognition"):
  Abstract: In this work, we try to perform Named Entity Recognition (NER) with external knowledge. We formulate the NER task as a multi-answer question answering (MAQA) task and provide different knowledge contexts, such as entity types, questions, definitions, and definitions with examples. Moreover, the formulation of the task as a MAQA task helps to reduce other errors. This formulation (a) enables systems to jointly learn from varied NER datasets, enabling systems to learn more NER specific features, (b) can use knowledge-text attention to identify words having higher similarity to 'entity type' mentioned in the knowledge, improving performance, (c) reduces confusion in systems by reducing the classes to be predicted, limited to only three (B, I, O), (d) Makes detection of Nested Entities easier. We perform extensive experiments of this Knowledge Guided NER (KGNER) formulation on 15 Biomedical NER datasets, and through these experiments, we see external knowledge helps. We will release the code for dataset conversion and our trained models for replicating experiments.

Includes members:
  Member "biobert a pre trained biomedical language representation model for biomedical text mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "a survey on recent advances in named entity recognition from deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "span based joint entity and relation extraction with transformer pre training")
  Member "scibert a pretrained language model for scientific text":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "question answering is a format when is it useful":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "scibert pretrained contextualized embeddings for scientific text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "enhancing clinical concept extraction with contextual embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "federated pretraining and fine tuning of bert using clinical notes from multiple silos")
  Member "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "careful selection of knowledge to solve open book question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "natural language qa approaches using reasoning with external knowledge")

Question: Please tell me which class the source paper 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)