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 as reduction"
  Abstract: We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we can perform discontinuous parsing in a very natural manner. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with strong baselines, such as the Berkeley parser for English and the best single system in the SPMRL-2014 shared task. Results are particularly striking for discontinuous parsing of German, where we surpass the current state of the art by a wide margin.

Connected through hyperedge HE-1 (induced by source paper "discontinuous constituent parsing with pointer networks"):
  with members:
    - "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"
    - "left to right dependency parsing with pointer networks"
    - "unlexicalized transition based discontinuous constituency parsing"
    - "stack pointer networks for dependency parsing"
    - "simpler but more accurate semantic dependency parsing"
    - "recurrent neural network grammars"
    - "deep biaffine attention for neural dependency parsing"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (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-3 (induced by source paper "sequence labeling parsing by learning across representations"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "left to right dependency parsing with pointer networks"
    - "multitask parsing across semantic representations"
    - "constituent parsing as sequence labeling"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-4 (induced by source paper "unlexicalized transition based discontinuous constituency 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"
    - "optimizing spectral learning for parsing"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-5 (induced by source paper "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"):
  with members:
    - "unlexicalized transition based discontinuous constituency parsing"
    - "dynamic oracles for top down and in order shift reduce constituent parsing"
    - "a minimal span based neural constituency parser"
    - "arc swift a novel transition system for dependency parsing"
    - "non projective dependency parsing with non local transitions"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-6 (induced by source paper "constituent parsing as sequence labeling"):
  with members:
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "optimizing spectral learning for parsing"

Connected through hyperedge HE-7 (induced by source paper "nightmare at test time how punctuation prevents parsers from generalizing"):
  with members:
    - "towards linguistically generalizable nlp systems a workshop and shared task"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-8 (induced by source paper "a transition based directed acyclic graph parser for ucca"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "hume human ucca based evaluation of machine translation"
    - "greedy joint syntactic semantic parsing with stack lstms"
    - "globally normalized transition based neural networks"
    - "an incremental parser for abstract meaning representation"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "robust subgraph generation improves abstract meaning representation parsing"
    - "adam a method for stochastic optimization"

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)