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: "train once test anywhere zero shot learning for text classification"
  Abstract: Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and embedding of sentence's tags. Learning such relationship makes the model generalize to unseen sentences, tags, and even new datasets provided they can be put into same embedding space. The model learns to predict whether a given sentence is related to a tag or not; unlike other classifiers that learn to classify the sentence as one of the possible classes. We propose three different neural networks for the task and report their accuracy on the test set of the dataset used for training them as well as two other standard datasets for which no retraining was done. We show that our models generalize well across new unseen classes in both cases. Although the models do not achieve the accuracy level of the state of the art supervised models, yet it evidently is a step forward towards general intelligence in natural language processing.

Connected through hyperedge HE-1 (induced by source paper "integrating semantic knowledge to tackle zero shot text classification"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a large scale attribute dataset for zero shot learning"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data augmentation for low resource neural machine translation"
    - "data noising as smoothing in neural network language models"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "i2t2i learning text to image synthesis with textual data augmentation"

Connected through hyperedge HE-2 (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"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "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"
    - "improved training of wasserstein gans"
    - "towards automated icd coding using deep learning"

Connected through hyperedge HE-3 (induced by source paper "benchmarking zero shot text classification datasets evaluation and entailment approach"):
  with members:
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "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"
    - "zero shot relation extraction via reading comprehension"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-4 (induced by source paper "an evaluation of transfer learning for classifying sales engagement emails at large scale"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "multi task deep neural networks for natural language understanding"
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "no training required exploring random encoders for sentence classification"
    - "practical text classification with large pre trained language models"
    - "deep contextualized word representations"
    - "joint embedding of words and labels for text classification"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"

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)