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: "parsing with the shortest derivation"
  Abstract: Common wisdom has it that the bias of stochastic grammars in favor of shorter derivations of a sentence is harmful and should be redressed. We show that the common wisdom is wrong for stochastic grammars that use elementary trees instead of context-free rules, such as Stochastic Tree-Substitution Grammars used by Data-Oriented Parsing models. For such grammars a non-probabilistic metric based on the shortest derivation outperforms a probabilistic metric on the ATIS and OVIS corpora, while it obtains very competitive results on the Wall Street Journal corpus. This paper also contains the first published experiments with DOP on the Wall Street Journal.

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"
    - "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 "bootstrapping structure into language alignment based learning"):
  with members:
    - "bootstrapping structure using similarity"
    - "bootstrapping syntax and recursion using alignment based learning"
    - "abl alignment based learning"
    - "an efficient probabilistically sound algorithm for segmentation and word discovery"
    - "learning efficient disambiguation"
    - "supervised grammar induction using training data with limited constituent information"
    - "identifying hierarchical structure in sequences a linear time algorithm"
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-3 (induced by source paper "what is the minimal set of fragments that achieves maximal parse accuracy"):
  with members:
    - "combining semantic and syntactic structure for language modeling"
    - "tree gram parsing lexical dependencies and structural relations"
    - "an improved parser for data oriented lexical functional analysis"
    - "learning efficient disambiguation"

Connected through hyperedge HE-4 (induced by source paper "combining semantic and syntactic structure for language modeling"):
  with members:
    - "exploiting syntactic structure for natural language modeling"

Connected through hyperedge HE-5 (induced by source paper "do all fragments count"):
  with members:
    - "combining semantic and syntactic structure for language modeling"
    - "tree gram parsing lexical dependencies and structural relations"
    - "an improved parser for data oriented lexical functional analysis"
    - "aspects of pattern matching in data oriented parsing"

Connected through hyperedge HE-6 (induced by source paper "an improved parser for data oriented lexical functional analysis"):
  with members:
    - "combining semantic and 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)