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: "fast reading comprehension with convnets"
  Abstract: State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelization within an instances and often becomes the bottleneck for deploying such models to latency critical scenarios. This is particularly problematic for longer texts. Here we present a convolutional architecture as an alternative to these recurrent architectures. Using simple dilated convolutional units in place of recurrent ones, we achieve results comparable to the state of the art on two question answering tasks, while at the same time achieving up to two orders of magnitude speedups for question answering.

Connected through hyperedge HE-1 (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-2 (induced by source paper "integrated triaging for fast reading comprehension"):
  with members:
    - "fastfusionnet new state of the art for dawnbench squad"
    - "phrase indexed question answering a new challenge for scalable document comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-3 (induced by source paper "building a robust text classifier on a test time budget"):
  with members:
    - "neural speed reading via skim rnn"
    - "learned in translation contextualized word vectors"
    - "learning to skim text"
    - "very deep convolutional networks for natural language processing"
    - "very deep convolutional networks for text classification"
    - "rationalizing neural predictions"
    - "conditional computation in neural networks for faster models"
    - "learning dynamic feature selection for fast sequential prediction"

Connected through hyperedge HE-4 (induced by source paper "robust text classifier on test time budgets"):
  with members:
    - "adaptive neural networks for efficient inference"
    - "learned in translation contextualized word vectors"
    - "learning to skim text"
    - "very deep convolutional networks for natural language processing"
    - "rationalizing neural predictions"
    - "conditional computation in neural networks for faster models"
    - "character level convolutional networks for text classification"
    - "effective approaches to attention based neural machine translation"

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)