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: "moving beyond the turing test with the allen ai science challenge"
  Abstract: Given recent successes in AI (e.g., AlphaGo's victory against Lee Sedol in the game of GO), it's become increasingly important to assess: how close are AI systems to human-level intelligence? This paper describes the Allen AI Science Challenge---an approach towards that goal which led to a unique Kaggle Competition, its results, the lessons learned, and our next steps.

Connected through hyperedge HE-1 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "embedding uncertain knowledge graphs"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "engaging image captioning via personality"
    - "vqa visual question answering"
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-2 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project"):
  with members:
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "repurposing entailment for multi hop question answering tasks"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-3 (induced by source paper "piqa reasoning about physical commonsense in natural language"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "language models as knowledge bases"
    - "assessing bert s syntactic abilities"
    - "tool macgyvering tool construction using geometric reasoning"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "hake human activity knowledge engine"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-4 (induced by source paper "creating a neural pedagogical agent by jointly learning to review and assess"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "explaining explanations an overview of interpretability of machine learning"
    - "neural collaborative filtering"
    - "challenges for transparency"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "commonsense properties from query logs and question anwering forums"):
  with members:
    - "visir visual and semantic image label refinement"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "automatic extraction of commonsense locatednear knowledge"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "augmenting end to end dialog systems with commonsense knowledge"

Connected through hyperedge HE-6 (induced by source paper "deep learning a critical appraisal"):
  with members:
    - "robust physical world attacks on deep learning models"
    - "deep reinforcement learning that matters"
    - "learning transferable architectures for scalable image recognition"
    - "visual interaction networks"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"

Connected through hyperedge HE-7 (induced by source paper "think you have solved question answering try arc the ai2 reasoning challenge"):
  with members:
    - "question answering as global reasoning over semantic abstractions"
    - "annotation artifacts in natural language inference data"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "crowdsourcing multiple choice science questions"
    - "adversarial examples for evaluating reading comprehension systems"
    - "answering complex questions using open information extraction"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "newsqa a machine comprehension dataset"

Connected through hyperedge HE-8 (induced by source paper "improving retrieval based question answering with deep inference models"):
  with members:
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "markov logic networks for natural language question answering"
    - "a large annotated corpus for learning natural language inference"
    - "convolutional neural networks for sentence classification"

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.AI(Artificial Intelligence)