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: "multi task learning of pairwise sequence classification tasks over disparate label spaces"
  Abstract: We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for aspect- and topic-based sentiment analysis.

Connected through hyperedge HE-1 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "learning from noisy labels with distillation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-2 (induced by source paper "an empirical evaluation of multi task learning in deep neural networks for natural language processing"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "constituency parsing with a self attentive encoder"
    - "neural network acceptability judgments"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "adversarial multi task learning for text classification"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a survey on multi task learning"

Connected through hyperedge HE-3 (induced by source paper "a joint named entity recognizer for heterogeneous tag setsusing a tag hierarchy"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for named entity recognition with neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "named entity recognition for novel types by transfer learning"
    - "de identification of patient notes with recurrent neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  with members:
    - "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-5 (induced by source paper "joint multi domain learning for automatic short answer grading"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial multi task learning for text classification"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "deep multitask learning for semantic dependency parsing"
    - "multi task multi domain representation learning for sequence tagging"
    - "multi task cross lingual sequence tagging from scratch"

Connected through hyperedge HE-6 (induced by source paper "seq2biseq bidirectional output wise recurrent neural networks for sequence modelling"):
  with members:
    - "measuring catastrophic forgetting in neural networks"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-7 (induced by source paper "claim extraction in biomedical publications using deep discourse model and transfer learning"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "causality extraction based on self attentive bilstm crf with transferred embeddings"
    - "allennlp a deep semantic natural language processing platform"
    - "fever a large scale dataset for fact extraction and verification"
    - "pubmed 200k rct a dataset for sequential sentence classification in medical abstracts"
    - "experiment segmentation in scientific discourse as clause level structured prediction using recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "dynamic multi level multi task learning for sentence simplification"
    - "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)