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: "adversarial deep averaging networks for cross lingual sentiment classification"
  Abstract: In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists. ADAN has two discriminative branches: a sentiment classifier and an adversarial language discriminator. Both branches take input from a shared feature extractor to learn hidden representations that are simultaneously indicative for the classification task and invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "calibration of pre trained transformers"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "calibration of encoder decoder models for neural machine translation"
    - "simplified neural unsupervised domain adaptation"
    - "evaluating lottery tickets under distributional shifts"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "revealing the dark secrets of bert"
    - "how to fine tune bert for text classification"

Connected through hyperedge HE-2 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "spectral normalization for generative adversarial networks"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "cross lingual distillation for text classification"
    - "aspect augmented adversarial networks for domain adaptation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "adapting deep learning for sentiment classification of code switched informal short text"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptation through self supervision"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "domain generalization by solving jigsaw puzzles"
    - "boosting supervision with self supervision for few shot learning"
    - "stabilizing gan training with multiple random projections"
    - "deep visual domain adaptation a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-5 (induced by source paper "adversarial domain adaptation for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "adversarial domain adaptation for duplicate question detection"
    - "multi task learning with sample re weighting for machine reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "cross lingual transfer learning for question answering"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-7 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-8 (induced by source paper "domain agnostic question answering with adversarial training"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "learning and evaluating general linguistic intelligence"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "multinomial adversarial networks for multi domain text classification"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "the web as a knowledge base for answering complex questions"
    - "allennlp a deep semantic natural language processing platform"

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)