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 "doc2edag an end to end document level framework for chinese financial event extraction"):
  Abstract: Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sentences, and even multiple such event mentions frequently co-exist in the same document. To address these challenges, we propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE (DEE) effectively. Moreover, we reformalize a DEE task with the no-trigger-words design to ease the document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we build a large-scale real-world dataset consisting of Chinese financial announcements with the challenges mentioned above. Extensive experiments with comprehensive analyses illustrate the superiority of Doc2EDAG over state-of-the-art methods. Data and codes can be found at this https URL.

Includes members:
  Member "one for all neural joint modeling of entities and events":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "training with streaming annotation")
  Member "fonduer knowledge base construction from richly formatted data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "document level n ary relation extraction with multiscale representation learning")
  Member "event extraction with generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "open event extraction from online text using a generative adversarial network")
  Member "reinforcement learning for relation classification from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dialog state tracking with reinforced data augmentation")
  Member "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving distant supervised relation extraction by dynamic neural network")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "joint extraction of entities and relations based on a novel tagging scheme":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "joint extraction of events and entities within a document context":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint event extraction along shortest dependency paths using graph convolutional networks")

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.CL(Computation and Language)