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: "deep biaffine attention for neural dependency parsing"
  Abstract: This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and 2.2%---and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches.

Connected through hyperedge HE-1 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "do we really need fully unsupervised cross lingual embeddings"

Connected through hyperedge HE-2 (induced by source paper "deeper task specificity improves joint entity and relation extraction"):
  with members:
    - "span based joint entity and relation extraction with transformer pre training"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "branched multi task networks deciding what layers to share"
    - "entity relation extraction as multi turn question answering"
    - "a general framework for information extraction using dynamic span graphs"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing"):
  with members:
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "75 languages 1 model parsing universal dependencies universally"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "constituency parsing with a self attentive encoder"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "convolution attention and structure embedding"):
  with members:
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "position based content attention for time series forecasting with sequence to sequence rnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "xception deep learning with depthwise separable convolutions"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-6 (induced by source paper "parsing thai social data a new challenge for thai nlp"):
  with members:
    - "a simple lstm model for transition based dependency parsing"
    - "semantic tagging with deep residual networks"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "a universal part of speech tagset"

Connected through hyperedge HE-7 (induced by source paper "end to end emotion cause pair extraction via learning to link"):
  with members:
    - "syntax aware aspect level sentiment classification with proximity weighted convolution network"
    - "a question answering approach to emotion cause extraction"
    - "end to end memory networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "rna secondary structure prediction by learning unrolled algorithms"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "attention is all you need"
    - "optnet differentiable optimization as a layer in neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "deep unfolding model based inspiration of novel deep architectures"

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)