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: "matroid bandits fast combinatorial optimization with learning"
  Abstract: A matroid is a notion of independence in combinatorial optimization which is closely related to computational efficiency. In particular, it is well known that the maximum of a constrained modular function can be found greedily if and only if the constraints are associated with a matroid. In this paper, we bring together the ideas of bandits and matroids, and propose a new class of combinatorial bandits, matroid bandits. The objective in these problems is to learn how to maximize a modular function on a matroid. This function is stochastic and initially unknown. We propose a practical algorithm for solving our problem, Optimistic Matroid Maximization (OMM); and prove two upper bounds, gap-dependent and gap-free, on its regret. Both bounds are sublinear in time and at most linear in all other quantities of interest. The gap-dependent upper bound is tight and we prove a matching lower bound on a partition matroid bandit. Finally, we evaluate our method on three real-world problems and show that it is practical.

Connected through hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "online second price auction with semi bandit feedback under the non stationary setting"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "learning to optimize under non stationarity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-2 (induced by source paper "best arm identification for cascading bandits in the fixed confidence setting"):
  with members:
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for cascading bandits"
    - "thompson sampling algorithms for cascading bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "a variant of azuma s inequality for martingales with subgaussian tails"

Connected through hyperedge HE-3 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-4 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "unifying count based exploration and intrinsic motivation"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"

Connected through hyperedge HE-5 (induced by source paper "combinatorial sleeping bandits with fairness constraints"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "beyond the click through rate web link selection with multi level feedback"
    - "fair task allocation in crowdsourced delivery"
    - "fairness in learning classic and contextual bandits"
    - "fair algorithms for infinite and contextual bandits"
    - "rawlsian fairness for machine learning"
    - "combinatorial bandits revisited"
    - "bandits with knapsacks"

Connected through hyperedge HE-6 (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"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-7 (induced by source paper "thompson sampling for cascading bandits"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a tutorial on thompson sampling"
    - "online learning to rank in stochastic click models"
    - "thompson sampling for the mnl bandit"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"

Connected through hyperedge HE-8 (induced by source paper "thompson sampling algorithms for cascading bandits"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "thompson sampling for the mnl bandit"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading 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)