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: "a robust transformation based learning approach using ripple down rules for part of speech tagging"
  Abstract: In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.

Connected through hyperedge HE-1 (induced by source paper "frequency vs association for constraint selection in usage based construction grammar"):
  with members:
    - "modeling the complexity and descriptive adequacy of construction grammars"
    - "a universal part of speech tagset"

Connected through hyperedge HE-2 (induced by source paper "modeling the complexity and descriptive adequacy of construction grammars"):
  with members:
    - "a universal part of speech tagset"

Connected through hyperedge HE-3 (induced by source paper "a fast and accurate vietnamese word segmenter"):
  with members:
    - "from word segmentation to pos tagging for vietnamese"
    - "ripple down rules for question answering"
    - "state of the art vietnamese word segmentation"

Connected through hyperedge HE-4 (induced by source paper "an empirical study for vietnamese dependency parsing"):
  with members:
    - "globally normalized transition based neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "structured training for neural network transition based parsing"

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)