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: "constructing narrative event evolutionary graph for script event prediction"
  Abstract: Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "situation recognition with graph neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-3 (induced by source paper "fi gnn modeling feature interactions via graph neural networks for ctr prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "semi supervised compatibility learning across categories for clothing matching"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "personalizing graph neural networks with attention mechanism for session based recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "situation recognition with graph neural networks"
    - "inter session modeling for session based recommendation"
    - "session aware information embedding for e commerce product recommendation"
    - "attention is all you need"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"

Connected through hyperedge HE-5 (induced by source paper "session based recommendation with graph neural network"):
  with members:
    - "situation recognition with graph neural networks"
    - "session aware information embedding for e commerce product recommendation"
    - "neural attentive session based recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "the more you know using knowledge graphs for image classification"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "modeling event background for if then commonsense reasoning using context aware variational autoencoder"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "variational recurrent neural machine translation"
    - "hierarchical neural story generation"
    - "event2mind commonsense inference on events intents and reactions"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "natural language inference over interaction space"
    - "latent intention dialogue models"

Connected through hyperedge HE-7 (induced by source paper "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a structured learning approach to temporal relation extraction"
    - "cogcomptime a tool for understanding time in natural language text"
    - "roberta a robustly optimized bert pretraining approach"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reasoning about actions and state changes by injecting commonsense knowledge"

Connected through hyperedge HE-8 (induced by source paper "story ending prediction by transferable bert"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "incorporating structured commonsense knowledge in story completion"
    - "attention is all you need"

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)