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: "crowdsourcing question answer meaning representations"
  Abstract: We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A detailed qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, QA-SRL, and AMR) along with many previously under-resourced ones, including implicit arguments and relations. The QAMR data and annotation code is made publicly available to enable future work on how best to model these complex phenomena.

Connected through hyperedge HE-1 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-2 (induced by source paper "question answering is a format when is it useful"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "machine reading comprehension a literature review"
    - "latent retrieval for weakly supervised open domain question answering"
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "learning and evaluating general linguistic intelligence"
    - "compositional questions do not necessitate multi hop reasoning"
    - "entity relation extraction as multi turn question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"

Connected through hyperedge HE-3 (induced by source paper "domain agnostic question answering with adversarial training"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "learning and evaluating general linguistic intelligence"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "multinomial adversarial networks for multi domain text classification"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "the web as a knowledge base for answering complex questions"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-4 (induced by source paper "mrqa 2019 shared task evaluating generalization in reading comprehension"):
  with members:
    - "an exploration of data augmentation and sampling techniques for domain agnostic question answering"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "learning and evaluating general linguistic intelligence"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "incidental supervision from question answering signals"):
  with members:
    - "partial or complete that s the question"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"

Connected through hyperedge HE-6 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "crowdsourcing ground truth for medical relation extraction"
    - "an analysis of visual question answering algorithms"
    - "learning to disambiguate by asking discriminative questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"

Connected through hyperedge HE-7 (induced by source paper "supervised neural models revitalize the open relation extraction"):
  with members:
    - "neural open information extraction"
    - "deep active learning for named entity recognition"
    - "neural models for sequence chunking"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "unsupervised pretraining for sequence to sequence learning"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "partial or complete that s the question"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "cogcomptime a tool for understanding time in natural language text"
    - "understanding autoencoders with information theoretic concepts"
    - "exploiting partially annotated data for temporal relation extraction"
    - "a multi axis annotation scheme for event temporal relations"
    - "active learning with partial feedback"
    - "structured set matching networks for one shot part labeling"
    - "opening the black box of deep neural networks via information"

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)