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: "mobile keyboard input decoding with finite state transducers"
  Abstract: We propose a finite-state transducer (FST) representation for the models used to decode keyboard inputs on mobile devices. Drawing from learnings from the field of speech recognition, we describe a decoding framework that can satisfy the strict memory and latency constraints of keyboard input. We extend this framework to support functionalities typically not present in speech recognition, such as literal decoding, autocorrections, word completions, and next word predictions. #R##N#We describe the general framework of what we call for short the keyboard "FST decoder" as well as the implementation details that are new compared to a speech FST decoder. We demonstrate that the FST decoder enables new UX features such as post-corrections. Finally, we sketch how this decoder can support advanced features such as personalization and contextualization.

Connected through hyperedge HE-1 (induced by source paper "federated learning of n gram language models"):
  with members:
    - "approximating probabilistic models as weighted finite automata"
    - "federated learning strategies for improving communication efficiency"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "federated learning for mobile keyboard prediction"
    - "lstm a search space odyssey"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-2 (induced by source paper "federated learning of out of vocabulary words"):
  with members:
    - "federated heavy hitters discovery with differential privacy"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "federated learning for mobile keyboard prediction"
    - "lstm a search space odyssey"
    - "conversational contextual cues the case of personalization and history for response ranking"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-3 (induced by source paper "approximating probabilistic models as weighted finite automata"):
  with members:
    - "weighted automata extraction from recurrent neural networks via regression on state spaces"
    - "federated learning of n gram language models"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "entropy based pruning of backoff language models"

Connected through hyperedge HE-4 (induced by source paper "writing across the world s languages deep internationalization for gboard the google keyboard"):
  with members:
    - "federated learning of n gram language models"
    - "automatic keyboard layout design for low resource latin script languages"
    - "challenges of language technologies for the indigenous languages of the americas"
    - "federated learning for mobile keyboard prediction"

Connected through hyperedge HE-5 (induced by source paper "federated learning for mobile keyboard prediction"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lstm a search space odyssey"
    - "neural networks for text correction and completion in keyboard decoding"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized data"
    - "using the output embedding to improve language models"
    - "exploring the limits of language modeling"

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)