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: "global neural ccg parsing with optimality guarantees"
  Abstract: We introduce the first global recursive neural parsing model with optimality guarantees during decoding. To support global features, we give up dynamic programs and instead search directly in the space of all possible subtrees. Although this space is exponentially large in the sentence length, we show it is possible to learn an efficient A* parser. We augment existing parsing models, which have informative bounds on the outside score, with a global model that has loose bounds but only needs to model non-local phenomena. The global model is trained with a new objective that encourages the parser to explore a tiny fraction of the search space. The approach is applied to CCG parsing, improving state-of-the-art accuracy by 0.4 F1. The parser finds the optimal parse for 99.9% of held-out sentences, exploring on average only 190 subtrees.

Connected through hyperedge HE-1 (induced by source paper "tetra tagging word synchronous parsing with linear time inference"):
  with members:
    - "better faster stronger sequence tagging constituent parsers"
    - "multilingual constituency parsing with self attention and pre training"
    - "constituent parsing as sequence labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "recurrent neural network grammars"
    - "neural crf parsing"

Connected through hyperedge HE-2 (induced by source paper "automatic generation of high quality ccgbanks for parser domain adaptation"):
  with members:
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "deep contextualized word representations"
    - "langpro natural language theorem prover"
    - "a ccg parsing with a supertag and dependency factored model"
    - "deep biaffine attention for neural dependency parsing"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "improved semantic representations from tree structured long short term memory networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-3 (induced by source paper "the web as a knowledge base for answering complex questions"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "evaluating semantic parsing against a simple web based question answering model"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "reading wikipedia to answer open domain questions"

Connected through hyperedge HE-4 (induced by source paper "value based search in execution space for mapping instructions to programs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "representation learning for grounded spatial reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-5 (induced by source paper "policy shaping and generalized update equations for semantic parsing from denotations"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "data recombination for neural semantic parsing"
    - "deep reinforcement learning for mention ranking coreference models"
    - "improving information extraction by acquiring external evidence with reinforcement learning"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-6 (induced by source paper "amuse multilingual semantic parsing for question answering over linked data"):
  with members:
    - "universal semantic parsing"
    - "learning a natural language interface with neural programmer"
    - "semantic relatedness for all languages a comparative analysis of multilingual semantic relatedness using machine translation"
    - "question answering on freebase via relation extraction and textual evidence"
    - "compositional semantic parsing on semi structured tables"
    - "distributed representations of words and phrases and their compositionality"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-7 (induced by source paper "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"):
  with members:
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "training with exploration improves a greedy stack lstm parser"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "what do recurrent neural network grammars learn about syntax"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "a ccg parsing with a supertag and dependency factored model"):
  with members:
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "structured training for neural network transition based parsing"

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)