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 "wireless scheduling algorithms in complex environments"):
  Abstract: Efficient spectrum use in wireless sensor networks through spatial reuse requires effective models of packet reception at the physical layer in the presence of interference. Despite recent progress in analytic and simulations research into worst-case behavior from interference effects, these efforts generally assume geometric path loss and isotropic transmission, assumptions which have not been borne out in experiments. #R##N#Our paper aims to provide a methodology for grounding theoretical results into wireless interference in experimental reality. We develop a new framework for wireless algorithms in which distance-based path loss is replaced by an arbitrary gain matrix, typically obtained by measurements of received signal strength (RSS). Gain matrices allow for the modeling of complex environments, e.g., with obstacles and walls. We experimentally evaluate the framework in two indoors testbeds with 20 and 60 motes, and confirm superior predictive performance in packet reception rate for a gain matrix model over a geometric distance-based model. #R##N#At the heart of our approach is a new parameter $\zeta$ called metricity which indicates how close the gain matrix is to a distance metric, effectively measuring the complexity of the environment. A powerful theoretical feature of this parameter is that all known SINR scheduling algorithms that work in general metric spaces carry over to arbitrary gain matrices and achieve equivalent performance guarantees in terms of $\zeta$ as previously obtained in terms of the path loss constant. Our experiments confirm the sensitivity of $\zeta$ to the nature of the environment. Finally, we show analytically and empirically how multiple channels can be leveraged to improve metricity and thereby performance. We believe our contributions will facilitate experimental validation for recent advances in algorithms for physical wireless interference models.

Includes members:
  Member "beyond geometry towards fully realistic wireless models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  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 "online independent set beyond the worst case secretaries prophets and periods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust algorithms for the secretary problem")
  Member "nearly optimal bounds for distributed wireless scheduling in the sinr model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spanning trees with edge conflicts and wireless connectivity")
  Member "wireless capacity and admission control in cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "wireless capacity with oblivious power in general metrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "a constant factor approximation for wireless capacity maximization with power control in the sinr model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "on a game theoretic approach to capacity maximization in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reception capacity definitions game theory and hardness")

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)