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 "pareto optimal allocation of transactive energy at market equilibrium in distribution systems a constrained vector optimization approach"):
  Abstract: In a grid constrained transactive distribution system market, distribution locational marginal pricing DLMP is influenced by the distance from the substation to an energy user, thereby causing households that are further away from the substation to be charged more. The Jain index of fairness, which has been recently applied to alleviate this undesirable effect of inefficient energy allocations, is used in this research to quantify fairness. It is shown that the Jain index is strictly quasi-concave. A bilevel distributed mechanism is proposed, where at the lower level, auction mechanisms are invoked simultaneously at each aggregator to obtain energy costs under market equilibrium conditions. A constrained multi gradient ascent algorithm, Augmented Lagrangian Multigradient Approach ALMA, is proposed for implementation at the upper level to attain energy allocations that represent tradeoffs between efficiency and fairness. Theoretical issues pertaining to ALMA as a generic algorithm for constrained vector optimization are considered. It is shown that when the objectives are restricted to be strictly quasi concave functions and if the feasible region is convex, ALMA converges towards global Pareto optimality. The overall effectiveness of the proposed approach is confirmed through a set of MATLAB simulations implemented on a modified IEEE 37-bus system platform.

Includes members:
  Member "distributed bilevel energy allocation mechanism with grid constraints and hidden user information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distribution lmp based transactive day ahead market with variable renewable generation")
  Member "multi task learning as multi objective optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mti net multi scale task interaction networks for multi task learning")

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.CE(Computational Engineering, Finance, and Science)