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 natural language inference using external knowledge in the science questions domain"
  Abstract: Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention thanks to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge -- a central topic in artificial intelligence -- has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-to-graph based models, and discuss implications for the use of external knowledge in solving the NLI problem. Our model achieves the new state-of-the-art performance on the NLI problem over the SciTail science questions dataset.

Connected through hyperedge HE-1 (induced by source paper "graph4code a machine interpretable knowledge graph for code"):
  with members:
    - "open domain question answering using early fusion of knowledge bases and text"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "a survey of machine learning for big code and naturalness"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-2 (induced by source paper "kagnet knowledge aware graph networks for commonsense reasoning"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "does it make sense and why a pilot study for sense making and explanation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving question answering by commonsense based pre training"
    - "from recognition to cognition visual commonsense reasoning"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-3 (induced by source paper "infusing knowledge into the textual entailment task using graph convolutional networks"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "multi task deep neural networks for natural language understanding"
    - "sentence embeddings in nli with iterative refinement encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "i know what you want semantic learning for text comprehension"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-4 (induced by source paper "heuristics for interpretable knowledge graph contextualization"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural natural language inference models enhanced with external knowledge"
    - "a decomposable attention model for natural language inference"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "complex embeddings for simple link prediction"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-5 (induced by source paper "path based contextualization of knowledge graphs for textual entailment"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "i know what you want semantic learning for text comprehension"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural natural language inference models enhanced with external knowledge"
    - "a decomposable attention model for natural language inference"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-6 (induced by source paper "answering science exam questions using query rewriting with background knowledge"):
  with members:
    - "query expansion techniques for information retrieval a survey"
    - "improving question answering by commonsense based pre training"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "improving machine reading comprehension with general reading strategies"
    - "ncrf an open source neural sequence labeling toolkit"

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)