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: "a linear programming approximation of ac power flows"
  Abstract: Linear active-power-only DC power flow approximations are pervasive in the planning and control of power systems. However, these approximations fail to capture reactive power and voltage magnitudes, both of which are necessary in many applications to ensure voltage stability and AC power flow feasibility. This paper proposes linear-programming models (the LPAC models) that incorporate reactive power and voltage magnitudes in a linear power flow approximation. The LPAC models are built on a convex approximation of the cosine terms in the AC equations, as well as Taylor approximations of the remaining nonlinear terms. Experimental comparisons with AC solutions on a variety of standard IEEE and MatPower benchmarks show that the LPAC models produce accurate values for active and reactive power, phase angles, and voltage magnitudes. The potential benefits of the LPAC models are illustrated on two "proof-of-concept" studies in power restoration and capacitor placement.

Connected through hyperedge HE-1 (induced by source paper "chance constraints for improving the security of ac optimal power flow"):
  with members:
    - "ac feasibility on tree networks is np hard"

Connected through hyperedge HE-2 (induced by source paper "linear quadratic programming based optimal power flow using linear power flow and absolute loss approximations"):
  with members:
    - "optimal sizing and placement of distributed storage in low voltage networks"

Connected through hyperedge HE-3 (induced by source paper "online learning for network constrained demand response pricing in distribution systems"):
  with members:
    - "optimal load ensemble control in chance constrained optimal power flow"
    - "risk sensitive learning and pricing for demand response"
    - "ac feasibility on tree networks is np hard"

Connected through hyperedge HE-4 (induced by source paper "optimal sizing and placement of distributed storage in low voltage networks"):
  with members:
    - "ac opf in radial distribution networks parts i ii"

Connected through hyperedge HE-5 (induced by source paper "the qc relaxation theoretical and computational results on optimal power flow"):
  with members:
    - "ac feasibility on tree networks is np hard"
    - "equivalent relaxations of optimal power flow"

Connected through hyperedge HE-6 (induced by source paper "nesta the nicta energy system test case archive"):
  with members:
    - "ac feasibility on tree networks is np hard"

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.AI(Artificial Intelligence)