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 nugget detection with forward backward recurrent neural networks"
  Abstract: Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks.

Connected through hyperedge HE-1 (induced by source paper "saliency learning teaching the model where to pay attention"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "event detection with neural networks a rigorous empirical evaluation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "dependent gated reading for cloze style question answering"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "analyzing the behavior of visual question answering models"
    - "text understanding with the attention sum reader network"
    - "rationalizing neural predictions"

Connected through hyperedge HE-2 (induced by source paper "cost sensitive regularization for label confusion aware event detection"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "nugget proposal networks for chinese event detection"
    - "adaptive scaling for sparse detection in information extraction"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "attentional multi reading sarcasm detection"):
  with members:
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a large self annotated corpus for sarcasm"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-4 (induced by source paper "event detection with neural networks a rigorous empirical evaluation"):
  with members:
    - "deep contextualized word representations"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)