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: "episodic memory in lifelong language learning"
  Abstract: We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. We propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay and local adaptation to allow the model to continuously learn from new datasets. We also show that the space complexity of the episodic memory module can be reduced significantly (~50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. We consider an episodic memory component as a crucial building block of general linguistic intelligence and see our model as a first step in that direction.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"
    - "spanbert improving pre training by representing and predicting spans"

Connected through hyperedge HE-2 (induced by source paper "lamol language modeling for lifelong language learning"):
  with members:
    - "continual learning for sentence representations using conceptors"
    - "lifelong domain word embedding via meta learning"
    - "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 "lamal language modeling is all you need for lifelong language learning"):
  with members:
    - "continual learning for sentence representations using conceptors"
    - "lifelong domain word embedding via meta learning"
    - "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-4 (induced by source paper "tx ray quantifying and explaining model knowledge transfer in un supervised nlp"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "continual lifelong learning with neural networks a review"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "visual interaction with deep learning models through collaborative semantic inference"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"

Connected through hyperedge HE-5 (induced by source paper "progressive memory banks for incremental domain adaptation"):
  with members:
    - "oracle order robust adaptive continual learning"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "memory augmented dialogue management for task oriented dialogue systems"
    - "efficient lifelong learning with a gem"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "reinforced continual learning"
    - "memory replay gans learning to generate images from new categories without forgetting"

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)