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: "policy gradient as a proxy for dynamic oracles in constituency parsing"
  Abstract: Dynamic oracles provide strong supervision for training constituency parsers with exploration, but must be custom defined for a given parser's transition system. We explore using a policy gradient method as a parser-agnostic alternative. In addition to directly optimizing for a tree-level metric such as F1, policy gradient has the potential to reduce exposure bias by allowing exploration during training; moreover, it does not require a dynamic oracle for supervision. On four constituency parsers in three languages, the method substantially outperforms static oracle likelihood training in almost all settings. For parsers where a dynamic oracle is available (including a novel oracle which we define for the transition system of Dyer et al. 2016), policy gradient typically recaptures a substantial fraction of the performance gain afforded by the dynamic oracle.

Connected through hyperedge HE-1 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "concurrent parsing of constituency and dependency"):
  with members:
    - "sequence labeling parsing by learning across representations"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-3 (induced by source paper "better faster stronger sequence tagging constituent parsers"):
  with members:
    - "multilingual constituency parsing with self attention and pre training"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "deep contextualized word representations"
    - "constituent parsing as sequence labeling"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-4 (induced by source paper "multi task semantic dependency parsing with policy gradient for learning easy first strategies"):
  with members:
    - "backpropagating through structured argmax using a spigot"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "learning joint semantic parsers from disjoint data"
    - "simpler but more accurate semantic dependency parsing"
    - "a transition based directed acyclic graph parser for ucca"
    - "deep multitask learning for semantic dependency parsing"
    - "recurrent neural network grammars"
    - "a joint many task model growing a neural network for multiple nlp tasks"

Connected through hyperedge HE-5 (induced by source paper "multilingual constituency parsing with self attention and pre training"):
  with members:
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "two local models for neural constituent parsing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "improving neural parsing by disentangling model combination and reranking effects"

Connected through hyperedge HE-6 (induced by source paper "dynamic oracles for top down and in order shift reduce constituent parsing"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "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 with exploration improves a greedy stack lstm parser"

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)