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: "graph convolutional networks for named entity recognition"
  Abstract: In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of GCN. We perform a comparison among different NER architectures and show that the grammar of a sentence positively influences the results. Experiments on the ontonotes dataset demonstrate consistent performance improvements, without requiring heavy feature engineering nor additional language-specific knowledge.

Connected through hyperedge HE-1 (induced by source paper "self attentional models for lattice inputs"):
  with members:
    - "hybrid self attention network for machine translation"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "attention is all you need"
    - "on the fly operation batching in dynamic computation graphs"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "interpreting and understanding graph convolutional neural network using gradient based attribution methods"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational inductive biases deep learning and graph networks"
    - "a simple neural network module for relational reasoning"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-3 (induced by source paper "structural neural encoders for amr to text generation"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "toward abstractive summarization using semantic representations"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "a graph to sequence model for amr to text generation"
    - "deep graph convolutional encoders for structured data to text generation"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "improved neural machine translation with a syntax aware encoder and decoder"

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)