human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bilinear bandits with low rank structure"):
  Abstract: We introduce the bilinear bandit problem with low-rank structure where an action is a pair of arms from two different entity types, and the reward is a bilinear function of the known feature vectors of the arms. The problem is motivated by numerous applications in which the learner must recommend two different entity types as one action, such as a male / female pair in an online dating service. The unknown in the problem is a $d_1$ by $d_2$ matrix $\mathbf{\Theta}^*$ with rank $r \ll \min\{d_1,d_2\}$ governing the reward generation. Determination of $\mathbf{\Theta}^*$ with low-rank structure poses a significant challenge in finding the right exploration-exploitation tradeoff. In this work, we propose a new two-stage algorithm called "Explore-Subspace-Then-Refine" (ESTR). The first stage is an explicit subspace exploration, while the second stage is a linear bandit algorithm called "almost-low-dimensional OFUL" (LowOFUL) that exploits and further refines the estimated subspace via a regularization technique. We show that the regret of ESTR is $\tilde{O}((d_1+d_2)^{3/2} \sqrt{r T})$ (where $\tilde{O}$ hides logarithmic factors), which improves upon the regret of $\tilde{O}(d_1d_2\sqrt{T})$ of a naive linear bandit reduction. We conjecture that the regret bound of ESTR is unimprovable up to polylogarithmic factors.

Includes members:
  Member "factored bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bandits with adversarial scaling")
  Member "horde of bandits using gaussian markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "laplacian regularized graph bandits algorithms and theoretical analysis")
  Member "bernoulli rank 1 bandits for click feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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")
  Member "stochastic low rank bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bandit principal component analysis")
  Member "stochastic rank 1 bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bandits with adversarial scaling")
  Member "contextual bandits with latent confounders an nmf approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "warm starting bandits with side information from confounded data")
  Member "on context dependent clustering of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmenting visual question answering with semantic frame information in a multitask learning approach")
  Member "low rank bandits with latent mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning near optimal policies with low inherent bellman error")

Question: Please tell me which class the source paper 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)