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: "simpler but more accurate semantic dependency parsing"
  Abstract: While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.

Connected through hyperedge HE-1 (induced by source paper "efficient sentence embedding via semantic subspace analysis"):
  with members:
    - "sbert wk a sentence embedding method by dissecting bert based word models"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vector of locally aggregated word embeddings vlawe a novel document level representation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-4 (induced by source paper "amr parsing as sequence to graph transduction"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "cross lingual abstract meaning representation parsing"

Connected through hyperedge HE-5 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-6 (induced by source paper "semantic role labeling with iterative structure refinement"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "second order semantic dependency parsing with end to end neural networks"
    - "capturing argument interaction in semantic role labeling with capsule networks"
    - "benchmarking approximate inference methods for neural structured prediction"
    - "achieving human parity on automatic chinese to english news translation"
    - "polyglot semantic role labeling"
    - "image segmentation by iterative inference from conditional score estimation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-7 (induced by source paper "broad coverage semantic parsing as transduction"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "compositional semantic parsing across graphbanks"
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "learning joint semantic parsers from disjoint data"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "incidental supervision from question answering signals"):
  with members:
    - "partial or complete that s the question"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"

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)