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 rank 1 bandits"
  Abstract: We propose stochastic rank-$1$ bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of their values as a reward. The main challenge of the problem is that the individual values of the row and column are unobserved. We assume that these values are stochastic and drawn independently. We propose a computationally-efficient algorithm for solving our problem, which we call Rank1Elim. We derive a $O((K + L) (1 / \Delta) \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, and $\Delta$ is the minimum of the row and column gaps; under the assumption that the mean row and column rewards are bounded away from zero. To the best of our knowledge, we present the first bandit algorithm that finds the maximum entry of a rank-$1$ matrix whose regret is linear in $K + L$, $1 / \Delta$, and $\log n$. We also derive a nearly matching lower bound. Finally, we evaluate Rank1Elim empirically on multiple problems. We observe that it leverages the structure of our problems and can learn near-optimal solutions even if our modeling assumptions are mildly violated.

Connected through hyperedge HE-1 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "improved path length regret bounds for bandits"
    - "competing bandits the perils of exploration under competition"
    - "factored bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "bandit regret scaling with the effective loss range"

Connected through hyperedge HE-2 (induced by source paper "bilinear bandits with low rank structure"):
  with members:
    - "factored bandits"
    - "horde of bandits using gaussian markov random fields"
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic low rank bandits"
    - "contextual bandits with latent confounders an nmf approach"
    - "on context dependent clustering of bandits"
    - "low rank bandits with latent mixtures"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (induced by source paper "improved algorithm on online clustering of bandits"):
  with members:
    - "online clustering of contextual cascading bandits"
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic low rank bandits"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning to rank in e commerce search engine formalization analysis and application"):
  with members:
    - "online learning to rank in stochastic click models"
    - "multiple play bandits in the position based model"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-5 (induced by source paper "factored bandits"):
  with members:
    - "bernoulli rank 1 bandits for click feedback"
    - "double thompson sampling for dueling bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-6 (induced by source paper "customized nonlinear bandits for online response selection in neural conversation models"):
  with members:
    - "end to end task completion neural dialogue systems"
    - "bernoulli rank 1 bandits for click feedback"
    - "a deep reinforcement learning chatbot"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"

Connected through hyperedge HE-7 (induced by source paper "online learning to rank in stochastic click models"):
  with members:
    - "bernoulli rank 1 bandits for click feedback"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-8 (induced by source paper "stochastic low rank bandits"):
  with members:
    - "contextual bandits with latent confounders an nmf approach"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "online clustering of bandits"

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)