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 crf for supervised aspect extraction"
  Abstract: This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.

Connected through hyperedge HE-1 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis"):
  with members:
    - "context aware embedding for targeted aspect based sentiment analysis"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "an interactive multi task learning network for end to end aspect based sentiment analysis"
    - "neural aspect and opinion term extraction with mined rules as weak supervision"
    - "multi task learning for multi modal emotion recognition and sentiment analysis"
    - "doer dual cross shared rnn for aspect term polarity co extraction"
    - "latent variable sentiment grammar"

Connected through hyperedge HE-2 (induced by source paper "transferable multi domain state generator for task oriented dialogue systems"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "the natural language decathlon multitask learning as question answering"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "neural approaches to conversational ai"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"

Connected through hyperedge HE-3 (induced by source paper "a failure of aspect sentiment classifiers and an adaptive re weighting solution"):
  with members:
    - "review conversational reading comprehension"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "the impact of class rebalancing techniques on the performance and interpretation of defect prediction models"
    - "transformation networks for target oriented sentiment classification"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a systematic study of the class imbalance problem in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "open domain targeted sentiment analysis via span based extraction and classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-5 (induced by source paper "representative task self selection for flexible clustered lifelong learning"):
  with members:
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "gradient episodic memory for continual learning"
    - "a survey on multi task learning"
    - "neurogenesis inspired dictionary learning online model adaption in a changing world"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "learning without forgetting"

Connected through hyperedge HE-6 (induced by source paper "sentence embedding alignment for lifelong relation extraction"):
  with members:
    - "lifelong learning for sentiment classification"
    - "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"
    - "continual learning through synaptic intelligence"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-7 (induced by source paper "forward and backward knowledge transfer for sentiment classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "lifelong learning for sentiment classification"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "dada deep adversarial data augmentation for extremely low data regime classification"):
  with members:
    - "good semi supervised learning that requires a bad gan"
    - "a classification based study of covariate shift in gan distributions"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "generative adversarial residual pairwise networks for one shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial generation of training examples applications to moving vehicle license plate recognition"
    - "do gans actually learn the distribution an empirical study"
    - "using synthetic data to train neural networks is model based reasoning"

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)