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 "hybrid neural tagging model for open relation extraction"):
  Abstract: Open relation extraction (ORE) remains a challenge to obtain a semantic representation by discovering arbitrary relation tuples from the unstructured text. Conventional methods heavily depend on feature engineering or syntactic parsing, they are inefficient or error-cascading. Recently, leveraging supervised deep learning structures to address the ORE task is an extraordinarily promising way. However, there are two main challenges: (1) The lack of enough labeled corpus to support supervised training; (2) The exploration of specific neural architecture that adapts to the characteristics of open relation extracting. In this paper, to overcome these difficulties, we build a large-scale, high-quality training corpus in a fully automated way, and design a tagging scheme to assist in transforming the ORE task into a sequence tagging processing. Furthermore, we propose a hybrid neural network model (HNN4ORT) for open relation tagging. The model employs the Ordered Neurons LSTM to encode potential syntactic information for capturing the associations among the arguments and relations. It also emerges a novel Dual Aware Mechanism, including Local-aware Attention and Global-aware Convolution. The dual aware nesses complement each other so that the model can take the sentence-level semantics as a global perspective, and at the same time implement salient local features to achieve sparse annotation. Experimental results on various testing sets show that our model can achieve state-of-the-art performances compared to the conventional methods or other neural models.

Includes members:
  Member "an open world extension to knowledge graph completion models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia")
  Member "graph neural networks with generated parameters for relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "openki integrating open information extraction and knowledge bases with relation inference":
    also connected through other hyperedges:
      (none)
  Member "design challenges and misconceptions in neural sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking generalization of neural models a named entity recognition case study")
  Member "ordered neurons integrating tree structures into recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "a survey on open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizing natural language analysis through span relation representations")
  Member "neural open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction")
  Member "deep active learning for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain independent extraction of scientific concepts from research articles")

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)