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: "lifelong domain word embedding via meta learning"
  Abstract: Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks. General-purpose embeddings trained on large-scale corpora are often sub-optimal for domain-specific applications. However, domain-specific tasks often do not have large in-domain corpora for training high-quality domain embeddings. In this paper, we propose a novel lifelong learning setting for domain embedding. That is, when performing the new domain embedding, the system has seen many past domains, and it tries to expand the new in-domain corpus by exploiting the corpora from the past domains via meta-learning. The proposed meta-learner characterizes the similarities of the contexts of the same word in many domain corpora, which helps retrieve relevant data from the past domains to expand the new domain corpus. Experimental results show that domain embeddings produced from such a process improve the performance of the downstream tasks.

Connected through hyperedge HE-1 (induced by source paper "lamol language modeling for lifelong language learning"):
  with members:
    - "episodic memory in lifelong language learning"
    - "continual learning for sentence representations using conceptors"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "lifelong learning for sentiment classification"
    - "on training recurrent neural networks for lifelong learning"
    - "the natural language decathlon multitask learning as question answering"
    - "efficient lifelong learning with a gem"
    - "exploring the challenges towards lifelong fact learning"

Connected through hyperedge HE-2 (induced by source paper "lamal language modeling is all you need for lifelong language learning"):
  with members:
    - "episodic memory in lifelong language learning"
    - "continual learning for sentence representations using conceptors"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "lifelong learning for sentiment classification"
    - "on training recurrent neural networks for lifelong learning"
    - "the natural language decathlon multitask learning as question answering"
    - "efficient lifelong learning with a gem"
    - "exploring the challenges towards lifelong fact learning"

Connected through hyperedge HE-3 (induced by source paper "open world learning and application to product classification"):
  with members:
    - "efficient large scale domain classification with personalized attention"
    - "learning to teach"
    - "probabilistic model agnostic meta learning"
    - "doc deep open classification of text documents"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "induction networks for few shot text classification"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "investigating capsule networks with dynamic routing for text classification"
    - "zero shot user intent detection via capsule neural networks"
    - "diverse few shot text classification with multiple metrics"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-5 (induced by source paper "fusing vector space models for domain specific applications"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "phrase based neural unsupervised machine translation"
    - "comparative study of cnn and rnn for natural language processing"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "multichannel variable size convolution for sentence classification"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"):
  with members:
    - "policy gradients for contextual recommendations"
    - "meta learning a survey"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "dynamic meta embeddings for improved sentence representations"
    - "policy optimization with model based explorations"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "neural factorization machines for sparse predictive analytics"
    - "field aware factorization machines in a real world online advertising system"

Connected through hyperedge HE-7 (induced by source paper "lifelong spectral clustering"):
  with members:
    - "representative task self selection for flexible clustered lifelong learning"
    - "lifelong learning for sentiment classification"
    - "selective experience replay for lifelong learning"
    - "encoder based lifelong learning"
    - "unified spectral clustering with optimal graph"
    - "learning without forgetting"

Connected through hyperedge HE-8 (induced by source paper "forward and backward knowledge transfer for sentiment classification"):
  with members:
    - "lifelong learning for sentiment classification"
    - "lifelong learning crf for supervised aspect extraction"
    - "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)