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: "left to right dependency parsing with pointer networks"
  Abstract: We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building $n$ attachments, with $n$ being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2$n$-1 actions to $n$. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.

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"
    - "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"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-2 (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"
    - "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"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "sequential graph dependency parser"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "unsupervised recurrent neural network grammars"
    - "universal dependency parsing from scratch"
    - "non monotonic sequential text generation"
    - "learning deep generative models of graphs"
    - "the importance of generation order in language modeling"
    - "saliency based sequential image attention with multiset prediction"

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)