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: "stochastic answer networks for squad 2 0"
  Abstract: This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not. The extended SAN contains two components: a span detector and a binary classifier for judging whether the question is unanswerable, and both components are jointly optimized. Experiments show that SAN achieves the results competitive to the state-of-the-art on Stanford Question Answering Dataset (SQuAD) 2.0. To facilitate the research on this field, we release our code: https://github.com/kevinduh/san_mrc.

Connected through hyperedge HE-1 (induced by source paper "retrospective reader for machine reading comprehension"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "read verify machine reading comprehension with unanswerable questions"
    - "quac question answering in context"

Connected through hyperedge HE-2 (induced by source paper "sg net syntax guided machine reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "limit bert linguistic informed multi task bert"
    - "syntax aware neural semantic role labeling with supertags"
    - "global greedy dependency parsing"
    - "option comparison network for multiple choice reading comprehension"
    - "probing contextualized sentence representations with visual awareness"
    - "what does bert look at an analysis of bert s attention"
    - "dual co matching network for multi choice reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "relation module for non answerable prediction on question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-4 (induced by source paper "fast prototyping a dialogue comprehension system for nurse patient conversations on symptom monitoring"):
  with members:
    - "learning to write notes in electronic health records"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "building a conversational agent overnight with dialogue self play"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "multi task learning for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "a question focused multi factor attention network for question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "multi task learning with sample re weighting for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "read verify machine reading comprehension with unanswerable questions"

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)