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: "domain adaptation with adversarial training and graph embeddings"
  Abstract: The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.

Connected through hyperedge HE-1 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services"):
  with members:
    - "diversity based generalization for neural unsupervised text classification under domain shift"
    - "advances in pre training distributed word representations"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "effective approaches to attention based neural machine translation"
    - "learning multiple tasks with deep relationship networks"
    - "gated feedback recurrent neural networks"
    - "learning multiple tasks with multilinear relationship networks"

Connected through hyperedge HE-2 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains"):
  with members:
    - "recognising agreement and disagreement between stances with reason comparing networks"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "deep memory networks for attitude identification"
    - "stance detection with bidirectional conditional encoding"
    - "deep coral correlation alignment for deep domain adaptation"
    - "bag of tricks for efficient text classification"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"

Connected through hyperedge HE-3 (induced by source paper "on identifying hashtags in disaster twitter data"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "deep contextualized word representations"
    - "recurrent neural network for text classification with multi task learning"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-4 (induced by source paper "diversity based generalization for neural unsupervised text classification under domain shift"):
  with members:
    - "transfer learning for sequences via learning to collocate"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "using similarity measures to select pretraining data for ner"
    - "unsupervised data augmentation for consistency training"
    - "evaluating historical text normalization systems how well do they generalize"

Connected through hyperedge HE-6 (induced by source paper "dane domain adaptive network embedding"):
  with members:
    - "tag2vec learning tag representations in tag networks"
    - "sepne bringing separability to network embedding"
    - "learning structural node embeddings via diffusion wavelets"
    - "adversarial discriminative domain adaptation"
    - "graph based isometry invariant representation learning"
    - "node2vec scalable feature learning for networks"
    - "multi task domain adaptation for sequence tagging"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "unsupervised detection of sub events in large scale disasters"):
  with members:
    - "sub event detection from twitter streams as a sequence labeling problem"
    - "graph based semi supervised learning with convolution neural networks to classify crisis related tweets"
    - "empathi an ontology for emergency managing and planning about hazard crisis"
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "fasttext zip compressing text classification models"
    - "intelligent disaster response via social media analysis a survey"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "sub story detection in twitter with hierarchical dirichlet processes"

Connected through hyperedge HE-8 (induced by source paper "few shot tweet detection in emerging disaster events"):
  with members:
    - "infinite mixture prototypes for few shot learning"
    - "social media usage patterns during natural hazards"
    - "prototypical networks for few shot learning"
    - "applications of online deep learning for crisis response using social media information"
    - "matching networks for one shot learning"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "convolutional neural networks for sentence classification"
    - "processing social media messages in mass emergency a survey"

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.LG(Machine Learning)