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: "unsupervised cross domain word representation learning"
  Abstract: Meaning of a word varies from one domain to another. Despite this important domain dependence in word semantics, existing word representation learning methods are bound to a single domain. Given a pair of \emph{source}-\emph{target} domains, we propose an unsupervised method for learning domain-specific word representations that accurately capture the domain-specific aspects of word semantics. First, we select a subset of frequent words that occur in both domains as \emph{pivots}. Next, we optimize an objective function that enforces two constraints: (a) for both source and target domain documents, pivots that appear in a document must accurately predict the co-occurring non-pivots, and (b) word representations learnt for pivots must be similar in the two domains. Moreover, we propose a method to perform domain adaptation using the learnt word representations. Our proposed method significantly outperforms competitive baselines including the state-of-the-art domain-insensitive word representations, and reports best sentiment classification accuracies for all domain-pairs in a benchmark dataset.

Connected through hyperedge HE-1 (induced by source paper "pod positional dependency based word embedding for aspect term extraction"):
  with members:
    - "language models with transformers"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "aspect term extraction with history attention and selective transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "elixa a modular and flexible absa platform"

Connected through hyperedge HE-2 (induced by source paper "topic sensitive attention on generic corpora corrects sense bias in pretrained embeddings"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-3 (induced by source paper "learning to create sentence semantic relation graphs for multi document summarization"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "dynamic meta embeddings for improved sentence representations"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "adapting the neural encoder decoder framework from single to multi document summarization"
    - "multi document summarization using distributed bag of words model"
    - "improving multi document summarization via text classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "neural adaptation layers for cross domain named entity recognition"):
  with members:
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised sequence tagging with bidirectional language models"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "transfer learning for named entity recognition with neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-5 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "joint modeling of content and discourse relations in dialogues"
    - "jointly learning word embeddings and latent topics"
    - "tweet2vec character based distributed representations for social media"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"

Connected through hyperedge HE-6 (induced by source paper "double embeddings and cnn based sequence labeling for aspect extraction"):
  with members:
    - "think globally embed locally locally linear meta embedding of words"
    - "lifelong learning crf for supervised aspect extraction"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "convolutional sequence to sequence learning"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "neural structural correspondence learning for domain adaptation"
    - "exploiting similarities among languages for machine translation"
    - "marginalized denoising autoencoders for domain adaptation"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "learning domain sensitive and sentiment aware word embeddings"):
  with members:
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "name disambiguation in anonymized graphs using network embedding"
    - "neural semantic role labeling with dependency path embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "thumbs up sentiment classification using machine learning techniques"

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)