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: "declarative question answering over knowledge bases containing natural language text with answer set programming"
  Abstract: While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to $18\%$ performance gain when compared to standard MCQ solvers.

Connected through hyperedge HE-1 (induced by source paper "natural language qa approaches using reasoning with external knowledge"):
  with members:
    - "transformers as soft reasoners over language"
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "piqa reasoning about physical commonsense in natural language"
    - "language models as knowledge bases"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "understanding roles and entities datasets and models for natural language inference"
    - "qasc a dataset for question answering via sentence composition"

Connected through hyperedge HE-2 (induced by source paper "careful selection of knowledge to solve open book question answering"):
  with members:
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transforming question answering datasets into natural language inference datasets"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "answering science exam questions using query rewriting with background knowledge"
    - "coqa a conversational question answering challenge"
    - "quarel a dataset and models for answering questions about qualitative relationships"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-3 (induced by source paper "natural language generation for non expert users"):
  with members:
    - "generating natural language explanations for visual question answering using scene graphs and visual attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "automatic generation of natural language explanations"
    - "table to text generation by structure aware seq2seq learning"

Connected through hyperedge HE-4 (induced by source paper "a generate validate approach to answering questions about qualitative relationships"):
  with members:
    - "quarel a dataset and models for answering questions about qualitative relationships"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhanced lstm for natural language inference"
    - "a large annotated corpus for learning natural language inference"

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)