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 knapsack problem and budgeted truthful bipartite matching"):
  Abstract: Two related online problems: knapsack and truthful bipartite matching are considered. For these two problems, the common theme is how to `match' an arriving left vertex in an online fashion with any of the available right vertices, if at all, so as to maximize the sum of the value of the matched edges, subject to satisfying a sum-weight constraint on the matched left vertices. Assuming that the left vertices arrive in an uniformly random order (secretary model), two almost similar algorithms are proposed for the two problems, that are $2e$ competitive and $24$ competitive, respectively. The proposed online bipartite matching algorithm is also shown to be truthful: there is no incentive for any left vertex to misreport its bid/weight. Direct applications of these problems include job allocation with load balancing, generalized adwords, crowdsourcing auctions, and matching wireless users to cooperative relays in device-to-device communication enabled cellular network.

Includes members:
  Member "online budgeted truthful matching":
    also connected through other hyperedges:
      (none)
  Member "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "a survey on device to device communication in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous energy harvesting and information transmission in a mimo full duplex system a machine learning based design")
  Member "matching theory for future wireless networks fundamentals and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "algorithms for secretary problems on graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random order models")
  Member "a distributed merge and split algorithm for fair cooperation in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overlapping coalition formation games for emerging communication networks")

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)