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: "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.

Connected through hyperedge HE-1 (induced by source paper "combinatorial optimization of ac optimal power flow with discrete demands in radial networks"):
  with members:
    - "truthful mechanisms for combinatorial allocation of electric power in alternating current electric systems for smart grid"
    - "complex demand knapsack problems and incentives in ac power systems"

Connected through hyperedge HE-2 (induced by source paper "complex demand scheduling problem with application in smart grid"):
  with members:
    - "combinatorial optimization of ac optimal power flow with discrete demands in radial networks"
    - "truthful mechanisms for combinatorial allocation of electric power in alternating current electric systems for smart grid"
    - "online algorithm for demand response with inelastic demands and apparent power constraint"
    - "truthful mechanisms for combinatorial ac electric power allocation"
    - "complex demand knapsack problems and incentives in ac power systems"
    - "a mazing 2 eps approximation for unsplittable flow on a path"
    - "vector bin packing with multiple choice"

Connected through hyperedge HE-3 (induced by source paper "online algorithm for demand response with inelastic demands and apparent power constraint"):
  with members:
    - "truthful mechanisms for combinatorial allocation of electric power in alternating current electric systems for smart grid"
    - "cost minimizing online algorithms for energy storage management with worst case guarantee"
    - "online algorithms for information aggregation from distributed and correlated sources"
    - "truthful mechanisms for combinatorial ac electric power allocation"
    - "complex demand knapsack problems and incentives in ac power systems"
    - "online energy generation scheduling for microgrids with intermittent energy sources and co generation"

Connected through hyperedge HE-4 (induced by source paper "truthful mechanisms for combinatorial allocation of electric power in alternating current electric systems for smart grid"):
  with members:
    - "complex demand scheduling problem with application in smart grid"
    - "truthful mechanisms for combinatorial ac electric power allocation"
    - "complex demand knapsack problems and incentives in ac power systems"
    - "mechanisms for multi unit auctions"
    - "computationally feasible vcg mechanisms"

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.DS(Data Structures and Algorithms)