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: "option comparison network for multiple choice reading comprehension"
  Abstract: Multiple-choice reading comprehension (MCRC) is the task of selecting the correct answer from multiple options given a question and an article. Existing MCRC models typically either read each option independently or compute a fixed-length representation for each option before comparing them. However, humans typically compare the options at multiple-granularity level before reading the article in detail to make reasoning more efficient. Mimicking humans, we propose an option comparison network (OCN) for MCRC which compares options at word-level to better identify their correlations to help reasoning. Specially, each option is encoded into a vector sequence using a skimmer to retain fine-grained information as much as possible. An attention mechanism is leveraged to compare these sequences vector-by-vector to identify more subtle correlations between options, which is potentially valuable for reasoning. Experimental results on the human English exam MCRC dataset RACE show that our model outperforms existing methods significantly. Moreover, it is also the first model that surpasses Amazon Mechanical Turker performance on the whole dataset.

Connected through hyperedge HE-1 (induced by source paper "neuro symbolic architectures for context understanding"):
  with members:
    - "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "knowledge graph embedding for ecotoxicological effect prediction"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "driving datasets literature review"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "dream a challenge dataset and models for dialogue based reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "dual multi head co attention for multi choice reading comprehension"):
  with members:
    - "a survey on machine reading comprehension systems"
    - "multi task learning with multi head attention for multi choice reading comprehension"
    - "mmm multi stage multi task learning for multi choice reading comprehension"
    - "albert a lite bert for self supervised learning of language representations"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "dual co matching network for multi choice reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a co matching model for multi choice reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-4 (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"
    - "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"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"

Connected through hyperedge HE-5 (induced by source paper "xlnet generalized autoregressive pretraining for language understanding"):
  with members:
    - "improving question answering with external knowledge"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "unsupervised data augmentation for consistency training"
    - "roberta a robustly optimized bert pretraining approach"
    - "dual co matching network for multi choice reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "towards generalizable neuro symbolic systems for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "improving question answering with external knowledge"
    - "augmenting neural networks with first order logic"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "knowledge enhanced contextual word representations"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "sensebert driving some sense into bert"

Connected through hyperedge HE-7 (induced by source paper "improving machine reading comprehension via adversarial training"):
  with members:
    - "spanbert improving pre training by representing and predicting spans"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "exploring machine reading comprehension with explicit knowledge"
    - "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"

Connected through hyperedge HE-8 (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"
    - "sg net syntax guided machine 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"

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)