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: "stochastic structured prediction under bandit feedback"
  Abstract: Stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations, the learner receives an input, predicts an output structure, and receives partial feedback in form of a task loss evaluation of the predicted structure. We present applications of this learning scenario to convex and non-convex objectives for structured prediction and analyze them as stochastic first-order methods. We present an experimental evaluation on problems of natural language processing over exponential output spaces, and compare convergence speed across different objectives under the practical criterion of optimal task performance on development data and the optimization-theoretic criterion of minimal squared gradient norm. Best results under both criteria are obtained for a non-convex objective for pairwise preference learning under bandit feedback.

Connected through hyperedge HE-1 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"

Connected through hyperedge HE-2 (induced by source paper "preference based interactive multi document summarisation"):
  with members:
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "viana visual interactive annotation of argumentation"
    - "better rewards yield better summaries learning to summarise without references"
    - "improving abstraction in text summarization"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "counterfactual learning from human proofreading feedback for semantic parsing"
    - "can neural machine translation be improved with user feedback"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "active learning for interactive neural machine translation of data streams"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"

Connected through hyperedge HE-4 (induced by source paper "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"):
  with members:
    - "can neural machine translation be improved with user feedback"
    - "interactive learning from policy dependent human feedback"
    - "adversarial neural machine translation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "a deep reinforced model for abstractive summarization"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a reinforcement learning approach to interactive predictive neural machine translation"):
  with members:
    - "a user study on online adaptation of neural machine translation to human post edits"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "asynchronous methods for deep reinforcement learning"
    - "an actor critic algorithm for sequence prediction"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "april interactively learning to summarise by combining active preference learning and reinforcement learning"):
  with members:
    - "finding convincing arguments using scalable bayesian preference learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "a deep reinforced model for abstractive summarization"
    - "sequence level training with recurrent neural networks"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-7 (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"
    - "bandit structured prediction for learning from partial feedback in statistical machine translation"
    - "neural architecture search with reinforcement learning"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "a shared task on bandit learning for machine translation"):
  with members:
    - "an actor critic algorithm for sequence prediction"
    - "bandit structured prediction for learning from partial feedback in statistical machine translation"
    - "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"

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)