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: "sembleu a robust metric for amr parsing evaluation"
  Abstract: Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The only existing evaluation metric, Smatch (Cai and Knight, 2013), searches for one-to-one mappings between the nodes of two AMRs with a greedy hill-climbing algorithm, which leads to search errors. We propose SemBleu, a robust metric that extends BLEU (Papineni et al., 2002) to AMRs. It does not suffer from search errors and considers non-local correspondences in addition to local ones. SemBleu is fully content-driven and punishes situations where a system output does not preserve most information from the input. Preliminary experiments on both sentence and corpus levels show that SemBleu has slightly higher consistency with human judgments than Smatch. Our code and data at http://github. com/freesunshine0316/sembleu.

Connected through hyperedge HE-1 (induced by source paper "amr similarity metrics from principles"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "automatic accuracy prediction for amr parsing"
    - "structural neural encoders for amr to text generation"
    - "amr parsing as sequence to graph transduction"
    - "sema an extended semantic evaluation metric for amr"
    - "amr normalization for fairer evaluation"
    - "a graph to sequence model for amr to text generation"
    - "amr parsing as graph prediction with latent alignment"

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)