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: "online learning for effort reduction in interactive neural machine translation"
  Abstract: Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol. #R##N#We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations. #R##N#In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.

Connected through hyperedge HE-1 (induced by source paper "interactive predictive neural multimodal systems"):
  with members:
    - "direct speech to speech translation with a sequence to sequence model"
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "egocentric video description based on temporally linked sequences"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "video description using bidirectional recurrent neural networks"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-2 (induced by source paper "incremental adaptation of nmt for professional post editors a user study"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "compact personalized models for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "a user study on online adaptation of neural machine translation to human post edits"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "a neural interactive predictive system for multimodal sequence to sequence tasks"):
  with members:
    - "attention is all you need"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "a user study on online adaptation of neural machine translation to human post edits"
    - "six challenges for neural machine translation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "show attend and tell neural image caption generation with visual attention"
    - "rethinking the inception architecture for computer vision"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "demonstration of a neural machine translation system with online learning for translators"):
  with members:
    - "incremental adaptation of nmt for professional post editors a user study"
    - "achieving human parity on automatic chinese to english news translation"
    - "compact personalized models for neural machine translation"
    - "online learning for neural machine translation post editing"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "active learning for interactive neural machine translation of data streams"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "modeling coverage for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "theano a python framework for fast computation of mathematical expressions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural machine translation of rare words with subword units"
    - "microsoft coco captions data collection and evaluation server"

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)