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: "neural speed reading via skim rnn"
  Abstract: Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens. Skim-RNN gives computational advantage over an RNN that always updates the entire hidden state. Skim-RNN uses the same input and output interfaces as a standard RNN and can be easily used instead of RNNs in existing models. In our experiments, we show that Skim-RNN can achieve significantly reduced computational cost without losing accuracy compared to standard RNNs across five different natural language tasks. In addition, we demonstrate that the trade-off between accuracy and speed of Skim-RNN can be dynamically controlled during inference time in a stable manner. Our analysis also shows that Skim-RNN running on a single CPU offers lower latency compared to standard RNNs on GPUs.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "sparseids learning packet sampling with reinforcement learning"):
  with members:
    - "explainability and adversarial robustness for rnns"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "learning to skim text"

Connected through hyperedge HE-3 (induced by source paper "fastfusionnet new state of the art for dawnbench squad"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "fast directional self attention mechanism"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"

Connected through hyperedge HE-4 (induced by source paper "how to fine tune bert for text classification"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "learning to remember more with less memorization"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "robust and scalable differentiable neural computer for question answering"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "learning to skim text"
    - "unbounded cache model for online language modeling with open vocabulary"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "neural approximation of an auto regressive process through confidence guided sampling"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "mask predict parallel decoding of conditional masked language models"
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "non monotonic sequential text generation"
    - "attentive neural processes"
    - "conditional neural processes"
    - "blockwise parallel decoding for deep autoregressive models"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-7 (induced by source paper "interactive machine comprehension with information seeking agents"):
  with members:
    - "episodic memory reader learning what to remember for question answering from streaming data"
    - "neural speed reading with structural jump lstm"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "learning to search in long documents using document structure"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning to search in long documents using document structure"):
  with members:
    - "towards inference oriented reading comprehension parallelqa"
    - "rainbow combining improvements in deep reinforcement learning"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "contextualized word representations for reading comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "the narrativeqa reading comprehension challenge"

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)