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: "transformer xl attentive language models beyond a fixed length context"
  Abstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.

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 "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "self attention with relative position representations"

Connected through hyperedge HE-4 (induced by source paper "time aware large kernel convolutions"):
  with members:
    - "accelerating large kernel convolution using summed area tables"
    - "pay less attention with lightweight and dynamic convolutions"
    - "massively multilingual neural machine translation"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "rethinking batch normalization in transformers"):
  with members:
    - "towards stabilizing batch statistics in backward propagation of batch normalization"
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a tensorized transformer for language modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "root mean square layer normalization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "understanding and improving layer normalization"

Connected through hyperedge HE-6 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "augmenting self attention with persistent memory"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "r transformer recurrent neural network enhanced transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-7 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification"):
  with members:
    - "relational word embeddings"
    - "kg bert bert for knowledge graph completion"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "k bert enabling language representation with knowledge graph"

Connected through hyperedge HE-8 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters"):
  with members:
    - "pretrained encyclopedia weakly supervised knowledge pretrained language model"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "kepler a unified model for knowledge embedding and pre trained language representation"
    - "language models as knowledge bases"
    - "parameter efficient transfer learning for nlp"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "matching the blanks distributional similarity for relation learning"

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)