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: "dcm bandits learning to rank with multiple clicks"
  Abstract: A search engine recommends to the user a list of web pages. The user examines this list, from the first page to the last, and clicks on all attractive pages until the user is satisfied. This behavior of the user can be described by the dependent click model (DCM). We propose DCM bandits, an online learning variant of the DCM where the goal is to maximize the probability of recommending satisfactory items, such as web pages. The main challenge of our learning problem is that we do not observe which attractive item is satisfactory. We propose a computationally-efficient learning algorithm for solving our problem, dcmKL-UCB; derive gap-dependent upper bounds on its regret under reasonable assumptions; and also prove a matching lower bound up to logarithmic factors. We evaluate our algorithm on synthetic and real-world problems, and show that it performs well even when our model is misspecified. This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "banditrank learning to rank using contextual bandits"):
  with members:
    - "reinforcement learning for online information seeking"
    - "banditsum extractive summarization as a contextual bandit"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi cast attention networks for retrieval based question answering and response prediction"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "bilateral multi perspective matching for natural language sentences"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "combinatorial bandits with relative feedback"):
  with members:
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "multi dueling bandits with dependent arms"
    - "thompson sampling for the mnl bandit"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"

Connected through hyperedge HE-4 (induced by source paper "unbiased learning to rank counterfactual and online approaches"):
  with members:
    - "optimizing ranking models in an online setting"
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "differentiable unbiased online learning to rank"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "consistent position bias estimation without online interventions for learning to rank"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "unbiased learning to rank with biased feedback"
    - "multiple play bandits in the position based model"

Connected through hyperedge HE-5 (induced by source paper "a contextual bandit approach to online learning to rank for relevance and diversity"):
  with members:
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "bubblerank safe online learning to re rank via implicit click feedback"
    - "merge double thompson sampling for large scale online ranker evaluation"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "differentiable unbiased online learning to rank"
    - "online learning to rank in stochastic click models"
    - "cascading bandits for large scale recommendation problems"

Connected through hyperedge HE-6 (induced by source paper "perturbed history exploration in stochastic multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic linear bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "customized nonlinear bandits for online response selection in neural conversation models"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "cascading bandits learning to rank in the cascade model"
    - "online stochastic linear optimization under one bit feedback"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-7 (induced by source paper "variance reduction in gradient exploration for online learning to rank"):
  with members:
    - "efficient exploration of gradient space for online learning to rank"
    - "differentiable unbiased online learning to rank"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "online learning to rank in stochastic click models"
    - "cascading bandits learning to rank in the cascade model"
    - "introducing letor 4 0 datasets"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "cascading non stationary bandits online learning to rank in the non stationary cascade model"):
  with members:
    - "merge double thompson sampling for large scale online ranker evaluation"
    - "online learning to rank in stochastic click models"
    - "multiple play bandits in the position based model"
    - "cascading bandits learning to rank in the cascade model"
    - "matroid bandits fast combinatorial optimization with learning"

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.LG(Machine Learning)