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: "a user study on online adaptation of neural machine translation to human post edits"
  Abstract: The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments. We present the first user study on online adaptation of NMT to user post-edits. Our study involves 29 human subjects whose post-editing effort and translation quality were measured on about 4,500 interactions of a human post-editor and a machine translation system integrating an online adaptive learning algorithm. Our experimental results show a significant reduction of human post-editing effort due to online adaptation in NMT according to several evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found significant improvements in BLEU/TER between NMT outputs and human references, and a strong correlation of these improvements with quality improvements of post-edits.

Connected through hyperedge HE-1 (induced by source paper "self regulated interactive sequence to sequence learning"):
  with members:
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "joey nmt a minimalist nmt toolkit for novices"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "learning to teach with dynamic loss functions"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "learning from chunk based feedback in neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "interactive predictive neural machine translation through reinforcement and imitation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "compact personalized models for neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-3 (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"
    - "online learning for effort reduction in interactive 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"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "a neural interactive predictive system for multimodal sequence to sequence tasks"):
  with members:
    - "online learning for effort reduction in interactive neural machine translation"
    - "attention is all you need"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "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-5 (induced by source paper "a reinforcement learning approach to interactive predictive neural machine translation"):
  with members:
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "asynchronous methods for deep reinforcement learning"
    - "an actor critic algorithm for sequence prediction"
    - "stochastic structured prediction under bandit feedback"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)