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 learning approach to shallow parsing"
  Abstract: A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.

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"
    - "noun phrase recognition by system combination"
    - "parsing with the shortest derivation"
    - "more accurate tests for the statistical significance of result differences"
    - "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"
    - "noun phrase recognition by system combination"
    - "tnt a statistical part of speech tagger"

Connected through hyperedge HE-3 (induced by source paper "the use of classifiers in sequential inference"):
  with members:
    - "a winnow based approach to context sensitive spelling correction"
    - "learning to resolve natural language ambiguities a unified approach"

Connected through hyperedge HE-4 (induced by source paper "using the distribution of performance for studying statistical nlp systems and corpora"):
  with members:
    - "more accurate tests for the statistical significance of result differences"
    - "learning to resolve natural language ambiguities a unified approach"

Connected through hyperedge HE-5 (induced by source paper "rule writing or annotation cost efficient resource usage for base noun phrase chunking"):
  with members:
    - "representing text chunks"
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-6 (induced by source paper "a classification approach to word prediction"):
  with members:
    - "measures of distributional similarity"
    - "a winnow based approach to context sensitive spelling correction"
    - "learning to resolve natural language ambiguities a unified approach"
    - "expoiting syntactic structure for language modeling"
    - "similarity based models of word cooccurrence probabilities"

Connected through hyperedge HE-7 (induced by source paper "applying system combination to base noun phrase identification"):
  with members:
    - "noun phrase recognition by system combination"
    - "learning to resolve natural language ambiguities a unified approach"
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-8 (induced by source paper "noun phrase recognition by system combination"):
  with members:
    - "representing text chunks"
    - "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.LG(Machine Learning)