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: "semi supervised multitask learning for sequence labeling"
  Abstract: We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.

Connected through hyperedge HE-1 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "continual lifelong learning with neural networks a review"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bridging theory and algorithm for domain adaptation"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"

Connected through hyperedge HE-2 (induced by source paper "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "text generation with exemplar based adaptive decoding"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "towards generating long and coherent text with multi level latent variable models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"

Connected through hyperedge HE-3 (induced by source paper "a multi task approach for disentangling syntax and semantics in sentence representations"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "disentangled representation learning for non parallel text style transfer"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "multimodal generative models for scalable weakly supervised learning"

Connected through hyperedge HE-4 (induced by source paper "detecting semantic anomalies"):
  with members:
    - "chester a web delivered locally computed chest x ray disease prediction system"
    - "data efficient image recognition with contrastive predictive coding"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deep anomaly detection using geometric transformations"

Connected through hyperedge HE-5 (induced by source paper "how to fine tune bert for text classification"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"
    - "neural speed reading via skim rnn"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "using context information to enhance simple question answering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "freebase triples a methodology for processing the freebase data dumps"
    - "attention is all you need"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "gcdt a global context enhanced deep transition architecture for sequence labeling"):
  with members:
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural reranking for named entity recognition"
    - "attention is all you need"
    - "neural models for sequence chunking"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-8 (induced by source paper "grn gated relation network to enhance convolutional neural network for named entity recognition"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for sequence tagging with hierarchical recurrent 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)