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: "measuring efficiency in high accuracy broad coverage statistical parsing"
  Abstract: Very little attention has been paid to the comparison of efficiency between high accuracy statistical parsers. This paper proposes one machine-independent metric that is general enough to allow comparisons across very different parsing architectures. This metric, which we call ``events considered'', measures the number of ``events'', however they are defined for a particular parser, for which a probability must be calculated, in order to find the parse. It is applicable to single-pass or multi-stage parsers. We discuss the advantages of the metric, and demonstrate its usefulness by using it to compare two parsers which differ in several fundamental ways.

Connected through hyperedge HE-1 (induced by source paper "framework and resources for natural language parser evaluation"):
  with members:
    - "a common xml based framework for syntactic annotations"
    - "joint and conditional estimation of tagging and parsing models"
    - "exploiting diversity in natural language processing combining parsers"
    - "atlas a flexible and extensible architecture for linguistic annotation"
    - "corpus annotation for parser evaluation"

Connected through hyperedge HE-2 (induced by source paper "probabilistic top down parsing and language modeling"):
  with members:
    - "exploiting syntactic structure for natural language modeling"
    - "exploiting diversity in natural language processing combining parsers"
    - "recognition performance of a structured language model"
    - "estimators for stochastic unification based grammars"
    - "efficient probabilistic top down and left corner parsing"
    - "refinement of a structured language model"
    - "expoiting syntactic structure for language modeling"

Connected through hyperedge HE-3 (induced by source paper "robust probabilistic predictive syntactic processing"):
  with members:
    - "probabilistic top down parsing and language modeling"
    - "exploiting syntactic structure for natural language modeling"
    - "compact non left recursive grammars using the selective left corner transform and factoring"
    - "recognition performance of a structured language model"
    - "efficient probabilistic top down and left corner parsing"
    - "refinement of a structured language model"
    - "expoiting syntactic structure for language 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)