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 "optimal power flow with inelastic demands for demand response in radial distribution networks"):
  Abstract: The classical optimal power flow (OPF) problem optimizes the power flow in a power network considering the associated flow and operating constraints. In this paper, we investigate OPF in the context of utility-maximizing demand response management in distribution networks, in which customers' demands are satisfied subject to the operating constraints of voltage and transmission power capacity. The prior results concern only elastic demands that can be partially satisfied, whereas power demands in practice can be inelastic with binary control decisions, which give rise to a mixed integer programming problem. We shed light on the hardness and approximability by polynomial-time algorithms for problem with inelastic demands. We show that this problem is inapproximable for general power network topology with upper and lower bounds of nodal voltage. Then, we propose an efficient algorithm for a relaxed problem in radial networks with bounded transmission power loss and upper bound of nodal voltage. We derive an approximation ratio between the proposed algorithm and the exact optimal solution. Simulations show that the proposed algorithm can produce close-to-optimal solutions in practice.

Includes members:
  Member "truthful mechanisms for combinatorial allocation of electric power in alternating current electric systems for smart grid":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial optimization of ac optimal power flow with discrete demands in radial networks")
  Member "ac feasibility on tree networks is np hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chance constraints for improving the security of ac optimal power flow")
  Member "truthful mechanisms for combinatorial ac electric power allocation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complex demand scheduling problem with application in smart grid")
  Member "complex demand knapsack problems and incentives in ac power systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial optimization of ac optimal power flow with discrete demands in radial 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)