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: "easy first dependency parsing with hierarchical tree lstms"
  Abstract: We suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders. To demonstrate its effectiveness, we use the representation as the backbone of a greedy, bottom-up dependency parser, achieving state-of-the-art accuracies for English and Chinese, without relying on external word embeddings. The parser's implementation is available for download at the first author's webpage.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for synthesizing functions in higher order logic"):
  with members:
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "cls smt bringing together combinatory logic synthesis and satisfiability modulo theories"
    - "hammering mizar by learning clause guidance"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "enigma efficient learning based inference guiding machine"
    - "tactictoe learning to reason with hol4 tactics"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "global greedy dependency parsing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "stack pointer networks for dependency parsing"

Connected through hyperedge HE-3 (induced by source paper "technical notes syntax aware representation learning with pointer networks"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "non projective dependency parsing via latent heads representation lhr"
    - "stack pointer networks for dependency parsing"
    - "scheduled multi task learning from syntax to translation"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "junction tree variational autoencoder for molecular graph generation"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "syntax directed variational autoencoder for structured data"
    - "deriving neural architectures from sequence and graph kernels"
    - "neural message passing for quantum chemistry"
    - "towards string to tree neural machine translation"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "effective representation for easy first dependency parsing"):
  with members:
    - "exploring recombination for efficient decoding of neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "syntax directed attention for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"
    - "deep biaffine attention for neural dependency parsing"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-6 (induced by source paper "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "an empirical study for vietnamese dependency parsing"
    - "enriching word vectors with subword information"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "named entity recognition with bidirectional lstm cnns"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "non projective dependency parsing via latent heads representation lhr"):
  with members:
    - "encoder decoder shift reduce syntactic parsing"
    - "recurrent additive networks"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "improving a strong neural parser with conjunction specific features"
    - "training with exploration improves a greedy stack lstm parser"
    - "dependency parsing as head selection"
    - "transition based dependency parsing with stack long short term memory"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-8 (induced by source paper "jointly learning sentence embeddings and syntax with unsupervised tree lstms"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "structured attention networks"
    - "attention is all you need"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "learning to compose task specific tree structures"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "overcoming catastrophic forgetting in neural networks"

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)