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: "arc swift a novel transition system for dependency parsing"
  Abstract: Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7--7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.

Connected through hyperedge HE-1 (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-2 (induced by source paper "designing and interpreting probes with control tasks"):
  with members:
    - "linspector multilingual probing tasks for word representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "assessing composition in sentence vector representations"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"

Connected through hyperedge HE-3 (induced by source paper "scene graph parsing by attention graph"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scene graph parsing as dependency parsing"
    - "attention is all you need"
    - "recurrent multimodal interaction for referring image segmentation"
    - "attention correctness in neural image captioning"
    - "spice semantic propositional image caption evaluation"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-4 (induced by source paper "recursive subtree composition in lstm based dependency parsing"):
  with members:
    - "deep contextualized word representations"
    - "colorless green recurrent networks dream hierarchically"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "an investigation of the interactions between pre trained word embeddings character models and pos tags in dependency parsing"
    - "recurrent neural network grammars"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "what do recurrent neural network grammars learn about syntax"

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"
    - "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"
    - "incremental parsing with minimal features using bi directional lstm"

Connected through hyperedge HE-6 (induced by source paper "parallelizable stack long short term memory"):
  with members:
    - "revisiting small batch training for deep neural networks"
    - "learning to parse and translate improves neural machine translation"
    - "on the fly operation batching in dynamic computation graphs"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "recurrent neural network grammars"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "a fast unified model for parsing and sentence understanding"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-7 (induced by source paper "left to right dependency parsing with pointer networks"):
  with members:
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "bi directional attention with agreement for dependency parsing"
    - "transition based dependency parsing with stack long short term memory"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "transition based parsing with lighter feed forward networks"):
  with members:
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "a non projective greedy dependency parser with bidirectional lstms"
    - "globally normalized transition based neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "yara parser a fast and accurate dependency 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)