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: "noun phrase recognition by system combination"
  Abstract: The performance of machine learning algorithms can be improved by combining the output of different systems. In this paper we apply this idea to the recognition of noun phrases.We generate different classifiers by using different representations of the data. By combining the results with voting techniques described in (Van Halteren et.al. 1998) we manage to improve the best reported performances on standard data sets for base noun phrases and arbitrary noun phrases.

Connected through hyperedge HE-1 (induced by source paper "memory based shallow parsing"):
  with members:
    - "what is the minimal set of fragments that achieves maximal parse accuracy"
    - "introduction to the conll 2000 shared task chunking"
    - "applying system combination to base noun phrase identification"
    - "parsing with the shortest derivation"
    - "more accurate tests for the statistical significance of result differences"
    - "a learning approach to shallow parsing"
    - "representing text chunks"
    - "cascaded markov models"

Connected through hyperedge HE-2 (induced by source paper "multidimensional transformation based learning"):
  with members:
    - "transformation based learning in the fast lane"
    - "coaxing confidences from an old friend probabilistic classifications from transformation rule lists"
    - "introduction to the conll 2000 shared task chunking"
    - "tnt a statistical part of speech tagger"
    - "a learning approach to shallow parsing"

Connected through hyperedge HE-3 (induced by source paper "learning computational grammars"):
  with members:
    - "combining a self organising map with memory based learning"
    - "introduction to the conll 2000 shared task chunking"
    - "applying system combination to base noun phrase identification"
    - "estimation of stochastic attribute value grammars using an informative sample"
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-4 (induced by source paper "applying system combination to base noun phrase identification"):
  with members:
    - "a learning approach to shallow parsing"
    - "learning to resolve natural language ambiguities a unified approach"
    - "forgetting exceptions is harmful in language learning"

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)