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: "integrating semantic knowledge to tackle zero shot text classification"
  Abstract: Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-2 (induced by source paper "human grounded evaluations of explanation methods for text classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "looking deeper into deep learning model attribution based explanations of textcnn"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "understanding convolutional neural networks for text classification"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "innvestigate neural networks"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "a human grounded evaluation benchmark for local explanations of machine learning"

Connected through hyperedge HE-3 (induced by source paper "generalized zero shot icd coding"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "large scale long tailed recognition in an open world"
    - "dual adversarial semantics consistent network for generalized zero shot learning"
    - "explainable prediction of medical codes from clinical text"
    - "multi modal cycle consistent generalized zero shot learning"
    - "train once test anywhere zero shot learning for text classification"
    - "improved training of wasserstein gans"
    - "towards automated icd coding using deep learning"

Connected through hyperedge HE-4 (induced by source paper "benchmarking zero shot text classification datasets evaluation and entailment approach"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "fever a large scale dataset for fact extraction and verification"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot user intent detection via capsule neural networks"
    - "train once test anywhere zero shot learning for text classification"
    - "zero shot relation extraction via reading comprehension"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-5 (induced by source paper "unsupervised annotation of phenotypic abnormalities via semantic latent representations on electronic health records"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention is all you need"
    - "the deep poincar e map a novel approach for left ventricle segmentation"
    - "i2t2i learning text to image synthesis with textual data augmentation"
    - "towards automated icd coding using deep learning"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

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)