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 "beyond geometry towards fully realistic wireless models"):
  Abstract: Signal-strength models of wireless communications capture the gradual fading of signals and the additivity of interference. As such, they are closer to reality than other models. However, nearly all theoretic work in the SINR model depends on the assumption of smooth geometric decay, one that is true in free space but is far off in actual environments. The challenge is to model realistic environments, including walls, obstacles, reflections and anisotropic antennas, without making the models algorithmically impractical or analytically intractable. #R##N#We present a simple solution that allows the modeling of arbitrary static situations by moving from geometry to arbitrary decay spaces. The complexity of a setting is captured by a metricity parameter Z that indicates how far the decay space is from satisfying the triangular inequality. All results that hold in the SINR model in general metrics carry over to decay spaces, with the resulting time complexity and approximation depending on Z in the same way that the original results depends on the path loss term alpha. For distributed algorithms, that to date have appeared to necessarily depend on the planarity, we indicate how they can be adapted to arbitrary decay spaces. #R##N#Finally, we explore the dependence on Z in the approximability of core problems. In particular, we observe that the capacity maximization problem has exponential upper and lower bounds in terms of Z in general decay spaces. In Euclidean metrics and related growth-bounded decay spaces, the performance depends on the exact metricity definition, with a polynomial upper bound in terms of Z, but an exponential lower bound in terms of a variant parameter phi. On the plane, the upper bound result actually yields the first approximation of a capacity-type SINR problem that is subexponential in alpha.

Includes members:
  Member "wireless scheduling algorithms in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the price of local power control in wireless scheduling")
  Member "jamming resistant learning in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "distributed deterministic broadcasting in uniform power ad hoc wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic protocols in the sinr model without knowledge of coordinates")
  Member "online independent set beyond the worst case secretaries prophets and periods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust algorithms for the secretary problem")
  Member "approximation algorithms for wireless link scheduling with flexible data rates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless link capacity under shadowing and fading")
  Member "distributed connectivity of wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "data dissemination in unified dynamic wireless networks")
  Member "mlls minimum length link scheduling under physical interference model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "link scheduling for throughput maximization in multihop wireless networks under physical interference")
  Member "the power of non uniform wireless power":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "data dissemination in unified dynamic wireless 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.NI(Networking and Internet Architecture)