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: "semi supervised sequence learning"
  Abstract: We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a "pretraining" step for a later supervised sequence learning algorithm. In other words, the parameters obtained from the unsupervised step can be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after being pretrained with the two approaches are more stable and generalize better. With pretraining, we are able to train long short term memory recurrent networks up to a few hundred timesteps, thereby achieving strong performance in many text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "contextual lensing of universal sentence representations"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ccmatrix mining billions of high quality parallel sentences on the web"
    - "parameter efficient transfer learning for nlp"
    - "unsupervised cross lingual representation learning at scale"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"

Connected through hyperedge HE-3 (induced by source paper "realm retrieval augmented language model pre training"):
  with members:
    - "how much knowledge can you pack into the parameters of a language model"
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "knowledge guided text retrieval and reading for open domain question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"

Connected through hyperedge HE-4 (induced by source paper "incorporating bert into neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "distilling the knowledge of bert for text generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "document level neural machine translation with hierarchical attention networks"

Connected through hyperedge HE-5 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "end to end neural entity linking"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-6 (induced by source paper "a financial service chatbot based on deep bidirectional transformers"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "selectivenet a deep neural network with an integrated reject option"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "roberta a robustly optimized bert pretraining approach"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "sampling based bayesian inference with gradient uncertainty"

Connected through hyperedge HE-7 (induced by source paper "multimodal story generation on plural images"):
  with members:
    - "deep contextualized word representations"
    - "long text generation via adversarial training with leaked information"
    - "show attend and tell neural image caption generation with visual attention"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "deep visual semantic alignments for generating image descriptions"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "detect deep trajectory clustering for mobility behavior analysis"):
  with members:
    - "autowarp learning a warping distance from unlabeled time series using sequence autoencoders"
    - "a survey of point of interest recommendation in location based social networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "unsupervised deep embedding for clustering analysis"
    - "vector field k means clustering trajectories by fitting multiple vector fields"

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)