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 "automatic identification of indicators of compromise using neural based sequence labelling"):
  Abstract: Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage. Thus, they exert an important role in the field of cybersecurity. However, state-of-the-art IOCs detection systems rely heavily on hand-crafted features with expert knowledge of cybersecurity, and require a large amount of supervised training corpora to train an IOC classifier. In this paper, we propose using a neural-based sequence labelling model to identify IOCs automatically from reports on cybersecurity without expert knowledge of cybersecurity. Our work is the first to apply an end-to-end sequence labelling to the task in IOCs identification. By using an attention mechanism and several token spelling features, we find that the proposed model is capable of identifying the low frequency IOCs from long sentences contained in cybersecurity reports. Experiments show that the proposed model outperforms other sequence labelling models, achieving over 88% average F1-score.

Includes members:
  Member "transfer learning for sequence tagging with hierarchical recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero resource cross domain named entity recognition")
  Member "transfer learning for named entity recognition with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero resource cross domain named entity recognition")
  Member "learning crosslingual word embeddings without bilingual corpora":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "attending to characters in neural sequence labeling models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task learning for argumentation mining")
  Member "bidirectional lstm crf models for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "named entity recognition with bidirectional lstm cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)