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 "no regret algorithms for online k submodular maximization"):
  Abstract: We present a polynomial time algorithm for online maximization of $k$-submodular maximization. For online (nonmonotone) $k$-submodular maximization, our algorithm achieves a tight approximate factor in an approximate regret. For online monotone $k$-submodular maximization, our approximate-regret matches to the best-known approximation ratio, which is tight asymptotically as $k$ tends to infinity. Our approach is based on the Blackwell approachability theorem and online linear optimization.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "an optimal algorithm for online unconstrained submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online non monotone dr submodular maximization")
  Member "derandomization for k submodular maximization":
    also connected through other hyperedges:
      (none)
  Member "maximizing k submodular functions and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved randomized algorithm for k submodular function maximization")
  Member "on maximizing a monotone k submodular function subject to a matroid constraint":
    also connected through other hyperedges:
      (none)
  Member "improved approximation algorithms for k submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved randomized algorithm for k submodular function maximization")
  Member "online submodular maximization under a matroid constraint with application to learning assignments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "powerset convolutional neural networks")
  Member "towards minimizing k submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond boolean surjective vcsps")

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.DS(Data Structures and Algorithms)