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: "event extraction with generative adversarial imitation learning"
  Abstract: We propose a new method for event extraction (EE) task based on an imitation learning framework, specifically, inverse reinforcement learning (IRL) via generative adversarial network (GAN). The GAN estimates proper rewards according to the difference between the actions committed by the expert (or ground truth) and the agent among complicated states in the environment. EE task benefits from these dynamic rewards because instances and labels yield to various extents of difficulty and the gains are expected to be diverse -- e.g., an ambiguous but correctly detected trigger or argument should receive high gains -- while the traditional RL models usually neglect such differences and pay equal attention on all instances. Moreover, our experiments also demonstrate that the proposed framework outperforms state-of-the-art methods, without explicit feature engineering.

Connected through hyperedge HE-1 (induced by source paper "doc2edag an end to end document level framework for chinese financial event extraction"):
  with members:
    - "one for all neural joint modeling of entities and events"
    - "fonduer knowledge base construction from richly formatted data"
    - "reinforcement learning for relation classification from noisy data"
    - "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "joint extraction of events and entities within a document context"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-2 (induced by source paper "open event extraction from online text using a generative adversarial network"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "spectral normalization for generative adversarial networks"
    - "adversarial ranking for language generation"
    - "improved training of wasserstein gans"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)