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: "sg net syntax guided machine reading comprehension"
  Abstract: For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. To verify its effectiveness, the proposed SG-Net is applied to typical pre-trained language model BERT which is right based on a Transformer encoder. Extensive experiments on popular benchmarks including SQuAD 2.0 and RACE show that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.

Connected through hyperedge HE-1 (induced by source paper "do multi hop question answering systems know how to answer the single hop sub questions"):
  with members:
    - "multi paragraph reasoning with knowledge enhanced graph neural network"
    - "select answer and explain interpretable multi hop reading comprehension over multiple documents"
    - "multi hop paragraph retrieval for open domain question answering"
    - "albert a lite bert for self supervised learning of language representations"
    - "dynamically fused graph network for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "hierarchical graph network for multi hop question answering"
    - "multi hop question answering via reasoning chains"

Connected through hyperedge HE-2 (induced by source paper "neural generators of sparse local linear models for achieving both accuracy and interpretability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "a unified approach to interpreting model predictions"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "probing contextualized sentence representations with visual awareness"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "semantics aware bert for language understanding"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "multi task deep neural networks for natural language understanding"
    - "glyce glyph vectors for chinese character representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-4 (induced by source paper "semantics aware bert for language understanding"):
  with members:
    - "limit bert linguistic informed multi task bert"
    - "dependency or span end to end uniform semantic role labeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-5 (induced by source paper "dual co matching network for multi choice reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "hierarchical contextualized representation for named entity recognition"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "evidence sentence extraction for machine reading comprehension"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "story ending prediction by transferable bert"

Connected through hyperedge HE-6 (induced by source paper "frustratingly easy natural question answering"):
  with members:
    - "span selection pre training for question answering"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "a bert baseline for the natural questions"
    - "roberta a robustly optimized bert pretraining approach"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-7 (induced by source paper "neural module networks for reasoning over text"):
  with members:
    - "giving bert a calculator finding operations and arguments with reading comprehension"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "a multi type multi span network for reading comprehension that requires discrete reasoning"
    - "neural compositional denotational semantics for question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "allennlp a deep semantic natural language processing platform"

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)