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: "forgetting exceptions is harmful in language learning"
  Abstract: We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.

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"
    - "a learning approach to shallow parsing"
    - "representing text chunks"

Connected through hyperedge HE-2 (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"
    - "noun phrase recognition by system combination"
    - "estimation of stochastic attribute value grammars using an informative sample"

Connected through hyperedge HE-3 (induced by source paper "rule writing or annotation cost efficient resource usage for base noun phrase chunking"):
  with members:
    - "a learning approach to shallow parsing"
    - "representing text chunks"

Connected through hyperedge HE-4 (induced by source paper "combining a self organising map with memory based learning"):
  with members:
    - "memory based shallow parsing"

Connected through hyperedge HE-5 (induced by source paper "a comparison between supervised learning algorithms for word sense disambiguation"):
  with members:
    - "naive bayes and exemplar based approaches to word sense disambiguation revisited"
    - "exploring automatic word sense disambiguation with decision lists and the web"
    - "boosting applied to word sense disambiguation"
    - "a winnow based approach to context sensitive spelling correction"
    - "learning to resolve natural language ambiguities a unified approach"

Connected through hyperedge HE-6 (induced by source paper "applying system combination to base noun phrase identification"):
  with members:
    - "noun phrase recognition by system combination"
    - "a learning approach to shallow parsing"
    - "learning to resolve natural language ambiguities a unified approach"

Connected through hyperedge HE-7 (induced by source paper "noun phrase recognition by system combination"):
  with members:
    - "a learning approach to shallow parsing"
    - "representing text chunks"

Connected through hyperedge HE-8 (induced by source paper "cascaded grammatical relation assignment"):
  with members:
    - "memory based shallow parsing"
    - "representing text chunks"

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)