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 domain adaptation for duplicate question detection"
  Abstract: We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.

Connected through hyperedge HE-1 (induced by source paper "adversarial domain adaptation for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "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"
    - "record bridging the gap between human and machine commonsense reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "interactive classification by asking informative questions"):
  with members:
    - "asking clarifying questions in open domain information seeking conversations"
    - "user intent prediction in information seeking conversations"
    - "joint learning of interactive spoken content retrieval and trainable user simulator"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "coqa a conversational question answering challenge"
    - "playing 20 question game with policy based reinforcement learning"
    - "learning to ask knowledge acquisition via 20 questions"
    - "quac question answering in context"

Connected through hyperedge HE-4 (induced by source paper "neural duplicate question detection without labeled training data"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "roberta a robustly optimized bert pretraining approach"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "injecting relational structural representation in neural networks for question similarity"
    - "an unsupervised model with attention autoencoders for question retrieval"
    - "harvesting paragraph level question answer pairs from wikipedia"

Connected through hyperedge HE-5 (induced by source paper "transfer learning for relation extraction via relation gated adversarial learning"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "see syntax aware entity embedding for neural relation extraction"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "robust semantic parsing with adversarial learning for domain generalization"):
  with members:
    - "sources of complexity in semantic frame parsing for information extraction"
    - "adversarial removal of demographic attributes from text data"
    - "semantic frame parsing for information extraction the calor corpus"
    - "linguistically informed self attention for semantic role labeling"
    - "deep semantic role labeling with self attention"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "adversarial multi task learning for text classification"
    - "word translation without parallel data"

Connected through hyperedge HE-7 (induced by source paper "dialog intent induction with deep multi view clustering"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "jointly deep multi view learning for clustering analysis"
    - "prototypical networks for few shot learning"
    - "a survey on multi view clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "multi domain dialog state tracking using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised domain adaptation of contextual embeddings for low resource duplicate question detection"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

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)