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 "online convex optimization for cumulative constraints"):
  Abstract: We propose an algorithm for online convex optimization which examines a clipped long-term constraint of the form $\sum\limits_{t=1}^T[g(x_t)]_+$, encoding the cumulative constraint violation. Previous literature has focused on long-term constraints of the form $\sum\limits_{t=1}^Tg(x_t)$, for which strictly feasible solutions can cancel out the effects of violated constraints.In this paper, we generalize the results in \cite{mahdavi2012trading} and \cite{jenatton2016adaptive} by showing that the clipped one $\sum\limits_{t=1}^T[g(x_t)]_+$ also has the same upper bound as the average one $\sum\limits_{t=1}^Tg(x_t)$. The performance of our proposed algorithm is also tested in experiments, in which we find that our algorithm can either follow the boundary of the constraints tightly or have relatively low clipped constraint violation.

Includes members:
  Member "global convergence of policy gradient methods for linearized control problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "projection free online learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")
  Member "trading regret for efficiency online convex optimization with long term constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploration exploitation in constrained mdps")
  Member "a stochastic view of optimal regret through minimax duality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "connections between mirror descent thompson sampling and the information ratio")

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)