human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "event representation learning enhanced with external commonsense knowledge"):
  Abstract: Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.

Includes members:
  Member "story ending prediction by transferable bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "temporal embeddings and transformer models for narrative text understanding")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "constructing narrative event evolutionary graph for script event prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "event representations with tensor based compositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tensor decompositions in deep learning")
  Member "deep biaffine attention for neural dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "a study of entanglement in a categorical framework of natural language":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the mathematics of text structure")
  Member "learning semantic script knowledge with event embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mcscript a novel dataset for assessing machine comprehension using script knowledge")
  Member "predicting abnormal returns from news using text classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a tensor based sub mode coordinate algorithm for stock prediction")

Question: Please tell me which class the source paper 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)