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: "bandit structured prediction for learning from partial feedback in statistical machine translation"
  Abstract: We present an approach to structured prediction from bandit feedback, called Bandit Structured Prediction, where only the value of a task loss function at a single predicted point, instead of a correct structure, is observed in learning. We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1-BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation. In our experiment bandit feedback is obtained by evaluating BLEU on reference translations without revealing them to the algorithm. This can be thought of as a simulation of interactive machine translation where an SMT system is personalized by a user who provides single point feedback to predicted translations. Our experiments show that our approach improves translation quality and is comparable to approaches that employ more informative feedback in learning.

Connected through hyperedge HE-1 (induced by source paper "warm starting contextual bandits robustly combining supervised and bandit feedback"):
  with members:
    - "active learning with logged data"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "corralling a band of bandit algorithms"
    - "active learning from weak and strong labelers"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "normalized online learning"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning for bandit neural machine translation with simulated human feedback"):
  with members:
    - "device placement optimization with reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "personalized machine translation preserving original author traits"
    - "asynchronous methods for deep reinforcement learning"
    - "an actor critic algorithm for sequence prediction"
    - "stochastic structured prediction under bandit feedback"
    - "neural architecture search with reinforcement learning"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a shared task on bandit learning for machine translation"):
  with members:
    - "an actor critic algorithm for sequence prediction"
    - "stochastic structured prediction under bandit feedback"
    - "edinburgh neural machine translation systems for wmt 16"
    - "neural machine translation of rare words with subword units"
    - "sequence level training with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "addressing the rare word problem in neural machine translation"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "stochastic structured prediction under bandit feedback"):
  with members:
    - "sequence level training with recurrent neural networks"
    - "learning to search better than your teacher"
    - "a contextual bandit approach to personalized news article recommendation"

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)