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: "supervised grammar induction using training data with limited constituent information"
  Abstract: Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is prohibitively expensive. This work aims to improve the induction strategy when there are few labels in the training data. We show that the most informative linguistic constituents are the higher nodes in the parse trees, typically denoting complex noun phrases and sentential clauses. They account for only 20% of all constituents. For inducing grammars from sparsely labeled training data (e.g., only higher-level constituent labels), we propose an adaptation strategy, which produces grammars that parse almost as well as grammars induced from fully labeled corpora. Our results suggest that for a partial parser to replace human annotators, it must be able to automatically extract higher-level constituents rather than base noun phrases.

Connected through hyperedge HE-1 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-2 (induced by source paper "extending a parser to distant domains using a few dozen partially annotated examples"):
  with members:
    - "a minimal span based neural constituency parser"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"
    - "effective inference for generative neural parsing"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "training dependency parsers with partial annotation"

Connected through hyperedge HE-3 (induced by source paper "fill it up exploiting partial dependency annotations in a minimum spanning tree parser"):
  with members:
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "a framework for under specifying dependency syntax without overloading annotators"
    - "a universal part of speech tagset"

Connected through hyperedge HE-4 (induced by source paper "training dependency parsers with partial annotation"):
  with members:
    - "probabilistic models for high order projective dependency parsing"

Connected through hyperedge HE-5 (induced by source paper "mining local gazetteers of literary chinese with crf and pattern based methods for biographical information in chinese history"):
  with members:
    - "mining and discovering biographical information in difangzhi with a language model based approach"

Connected through hyperedge HE-6 (induced by source paper "domain adaptation for statistical classifiers"):
  with members:
    - "a model of inductive bias learning"

Connected through hyperedge HE-7 (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"
    - "parsing with the shortest derivation"
    - "an efficient probabilistically sound algorithm for segmentation and word discovery"
    - "learning efficient disambiguation"
    - "identifying hierarchical structure in sequences a linear time algorithm"
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-8 (induced by source paper "using existing systems to supplement small amounts of annotated grammatical relations training data"):
  with members:
    - "exploiting diversity in natural language processing combining parsers"
    - "corpus annotation for parser evaluation"
    - "learning transformation rules to find grammatical relations"
    - "cascaded grammatical relation assignment"

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)