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: "improving question answering with external knowledge"
  Abstract: We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus. In this work, we explore simple yet effective methods for exploiting two sources of external knowledge for subject-area QA. The first enriches the original subject-area reference corpus with relevant text snippets extracted from an open-domain resource (i.e., Wikipedia) that cover potentially ambiguous concepts in the question and answer options. As in other QA research, the second method simply increases the amount of training data by appending additional in-domain subject-area instances. #R##N#Experiments on three challenging multiple-choice science QA tasks (i.e., ARC-Easy, ARC-Challenge, and OpenBookQA) demonstrate the effectiveness of our methods: in comparison to the previous state-of-the-art, we obtain absolute gains in accuracy of up to 8.1%, 13.0%, and 12.8%, respectively. While we observe consistent gains when we introduce knowledge from Wikipedia, we find that employing additional QA training instances is not uniformly helpful: performance degrades when the added instances exhibit a higher level of difficulty than the original training data. As one of the first studies on exploiting unstructured external knowledge for subject-area QA, we hope our methods, observations, and discussion of the exposed limitations may shed light on further developments in the area.

Connected through hyperedge HE-1 (induced by source paper "sirenless reveal the intention behind news"):
  with members:
    - "reading wikipedia to answer open domain questions"

Connected through hyperedge HE-2 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

Connected through hyperedge HE-3 (induced by source paper "xlnet generalized autoregressive pretraining for language understanding"):
  with members:
    - "option comparison network for multiple choice reading comprehension"
    - "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-4 (induced by source paper "what s missing a knowledge gap guided approach for multi hop question answering"):
  with members:
    - "understanding dataset design choices for multi hop reasoning"
    - "careful selection of knowledge to solve open book question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"

Connected through hyperedge HE-5 (induced by source paper "towards generalizable neuro symbolic systems for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "augmenting neural networks with first order logic"
    - "option comparison network for multiple choice reading comprehension"
    - "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-6 (induced by source paper "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"):
  with members:
    - "passage re ranking with bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-7 (induced by source paper "qasc a dataset for question answering via sentence composition"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "question answering as global reasoning over semantic abstractions"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what s missing a knowledge gap guided approach for multi hop question answering"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "from recognition to cognition visual commonsense reasoning"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-8 (induced by source paper "answering questions by learning to rank learning to rank by answering questions"):
  with members:
    - "improving retrieval based question answering with deep inference models"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to attend on essential terms an enhanced retriever reader model for open domain question answering"

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)