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 learning for sentiment classification"
  Abstract: This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from past tasks and use it to help future learning. In this paper, we first discuss LL in general and then LL for sentiment classification in particular. The proposed LL approach adopts a Bayesian optimization framework based on stochastic gradient descent. Our experimental results show that the proposed method outperforms baseline methods significantly, which demonstrates that lifelong learning is a promising research direction.

Connected through hyperedge HE-1 (induced by source paper "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "no training required exploring random encoders for sentence classification"
    - "topic memory networks for short text classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "deriving machine attention from human rationales"
    - "a structured self attentive sentence embedding"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (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"
    - "lifelong domain word embedding via meta learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "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 "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"
    - "lifelong domain word embedding via meta learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "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-4 (induced by source paper "psycholinguistics meets continual learning measuring catastrophic forgetting in visual question answering"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "continuous learning in single incremental task scenarios"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "inferring and executing programs for visual reasoning"
    - "toward continual learning for conversational agents"
    - "continual learning through synaptic intelligence"
    - "film visual reasoning with a general conditioning layer"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "sentence embedding alignment for lifelong relation extraction"):
  with members:
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "improved neural relation detection for knowledge base question answering"
    - "progressive neural networks"
    - "lifelong learning crf for supervised aspect extraction"
    - "continual learning through synaptic intelligence"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-6 (induced by source paper "lifelong spectral clustering"):
  with members:
    - "representative task self selection for flexible clustered lifelong learning"
    - "lifelong domain word embedding via meta learning"
    - "selective experience replay for lifelong learning"
    - "encoder based lifelong learning"
    - "unified spectral clustering with optimal graph"
    - "learning without forgetting"

Connected through hyperedge HE-7 (induced by source paper "towards automl in the presence of drift first results"):
  with members:
    - "large scale evolution of image classifiers"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "openml networked science in machine learning"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-8 (induced by source paper "forward and backward knowledge transfer for sentiment classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "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)