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: "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
  Abstract: Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a "black art that requires expert experiences" (Snoek et al., 2012). In this paper, we evaluate the importance of different network design choices and hyperparameters for five common linguistic sequence tagging tasks (POS, Chunking, NER, Entity Recognition, and Event Detection). We evaluated over 50.000 different setups and found, that some parameters, like the pre-trained word embeddings or the last layer of the network, have a large impact on the performance, while other parameters, for example the number of LSTM layers or the number of recurrent units, are of minor importance. We give a recommendation on a configuration that performs well among different tasks.

Connected through hyperedge HE-1 (induced by source paper "rethinking generalization of neural models a named entity recognition case study"):
  with members:
    - "a closer look at data bias in neural extractive summarization models"
    - "transfer learning for sequence labeling using source model and target data"
    - "stiffness a new perspective on generalization in neural networks"
    - "design challenges and misconceptions in neural sequence labeling"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "comparing rule based feature based and deep neural methods for de identification of dutch medical records"):
  with members:
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer learning for named entity recognition with neural networks"
    - "de identification of patient notes with recurrent neural networks"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-3 (induced by source paper "a comparative study of sequence classification models for privacy policy coverage analysis"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "named entity recognition with bidirectional lstm cnns"
    - "a c lstm neural network for text classification"
    - "convolutional neural networks for sentence classification"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"

Connected through hyperedge HE-5 (induced by source paper "hierarchically refined label attention network for sequence labeling"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "design challenges and misconceptions in neural sequence labeling"
    - "two local models for neural constituent parsing"
    - "joint embedding of words and labels for text classification"
    - "semi supervised sequence modeling with cross view training"
    - "cross target stance classification with self attention networks"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning approximate inference networks for structured prediction"

Connected through hyperedge HE-6 (induced by source paper "on the variance of the adaptive learning rate and beyond"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "fixup initialization residual learning without normalization"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "training tips for the transformer model"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "gated task interaction framework for multi task sequence tagging"):
  with members:
    - "aspect based sentiment analysis with gated convolutional networks"
    - "linguistically informed self attention for semantic role labeling"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "financial time series forecasting with deep learning a systematic literature review 2005 2019"):
  with members:
    - "financial trading model with stock bar chart image time series with deep convolutional neural networks"
    - "forecasting economics and financial time series arima vs lstm"
    - "sentiment predictability for stocks"
    - "lstm a search space odyssey"
    - "relational autoencoder for feature extraction"
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "searching for activation functions"

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)