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 "porous lattice based transformer encoder for chinese ner"):
  Abstract: Incorporating lattices into character-level Chinese named entity recognition is an effective method to exploit explicit word information. Recent works extend recurrent and convolutional neural networks to model lattice inputs. However, due to the DAG structure or the variable-sized potential word set for lattice inputs, these models prevent the convenient use of batched computation, resulting in serious inefficient. In this paper, we propose a porous lattice-based transformer encoder for Chinese named entity recognition, which is capable to better exploit the GPU parallelism and batch the computation owing to the mask mechanism in transformer. We first investigate the lattice-aware self-attention coupled with relative position representations to explore effective word information in the lattice structure. Besides, to strengthen the local dependencies among neighboring tokens, we propose a novel porous structure during self-attentional computation processing, in which every two non-neighboring tokens are connected through a shared pivot node. Experimental results on four datasets show that our model performs up to 9.47 times faster than state-of-the-art models, while is roughly on a par with its performance. The source code of this paper can be obtained from this https URL.

Includes members:
  Member "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the vietnamese name entity recognition a deep learning method approach")
  Member "lattice transformer for speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural zero inflated quality estimation model for automatic speech recognition system")
  Member "lattice based transformer encoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical contextualized representation for named entity recognition")
  Member "self attentional models for lattice inputs":
    also connected through other hyperedges:
      (none)
  Member "neural collective entity linking based on recurrent random walk network learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning dynamic context augmentation for global entity linking")
  Member "chinese ner using lattice lstm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self attention based bigru and capsule network for named entity recognition")
  Member "modeling localness for self attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gret global representation enhanced transformer")
  Member "convolutional self attention network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention augmented 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)