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 label embedding for text classification"
  Abstract: Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semi-supervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.

Connected through hyperedge HE-1 (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-2 (induced by source paper "unsupervised transfer learning via bert neuron selection"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "revealing the dark secrets of bert"
    - "linguistic knowledge and transferability of contextual representations"
    - "meta multi task learning for sequence modeling"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "tdam a topic dependent attention model for sentiment analysis"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "learning structured text representations"
    - "attention is all you need"
    - "the consciousness prior"
    - "adversarial multi task learning for text classification"

Connected through hyperedge HE-4 (induced by source paper "multi task networks with universe group and task feature learning"):
  with members:
    - "soft layer specific multi task summarization with entailment and question generation"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial multi task learning for text classification"
    - "transfer learning for neural semantic parsing"
    - "recurrent neural network for text classification with multi task learning"
    - "neural architectures for named entity recognition"
    - "domain separation networks"

Connected through hyperedge HE-5 (induced by source paper "the audio auditor participant level membership inference in internet of things voice services"):
  with members:
    - "the natural auditor how to tell if someone used your words to train their model"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "stealing hyperparameters in machine learning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-6 (induced by source paper "multi task learning for universal sentence representations what syntactic and semantic information is captured"):
  with members:
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "end to end neural coreference resolution"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"
    - "a generalized recurrent neural architecture for text classification with multi task learning"

Connected through hyperedge HE-7 (induced by source paper "learning robust transferable sentence representations for text classification"):
  with members:
    - "few shot text classification with pre trained word embeddings and a human in the loop"
    - "universal sentence encoder"
    - "an efficient framework for learning sentence representations"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-8 (induced by source paper "joint embedding of words and labels for text classification"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "explainable prediction of medical codes from clinical text"
    - "topic compositional neural language model"
    - "attention is all you need"
    - "attention over attention neural networks for reading comprehension"
    - "deconvolutional latent variable model for text sequence matching"
    - "convolutional sequence to sequence learning"
    - "deconvolutional paragraph representation learning"

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)