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: "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.

Connected through hyperedge HE-1 (induced by source paper "the price of local power control in wireless scheduling"):
  with members:
    - "how well can graphs represent wireless interference"
    - "beyond geometry towards fully realistic wireless models"
    - "on the impact of geometry on ad hoc communication in wireless networks"
    - "approximation algorithms for wireless link scheduling with flexible data rates"
    - "the power of non uniform wireless power"
    - "towards tight bounds for local broadcasting"
    - "on the complexity of the minimum latency scheduling problem on the euclidean plane"
    - "algorithms for wireless capacity"

Connected through hyperedge HE-2 (induced by source paper "beyond geometry towards fully realistic wireless models"):
  with members:
    - "jamming resistant learning in wireless networks"
    - "distributed deterministic broadcasting in uniform power ad hoc wireless networks"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "approximation algorithms for wireless link scheduling with flexible data rates"
    - "distributed connectivity of wireless networks"
    - "mlls minimum length link scheduling under physical interference model"
    - "the power of non uniform wireless power"
    - "a fully distributed algorithm for throughput performance in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "how well can graphs represent wireless interference"):
  with members:
    - "beyond geometry towards fully realistic wireless models"
    - "approximation algorithms for wireless link scheduling with flexible data rates"
    - "dynamic packet scheduling in wireless networks"
    - "on the complexity of the minimum latency scheduling problem on the euclidean plane"
    - "algorithms for wireless capacity"
    - "secondary spectrum auctions for symmetric and submodular bidders"
    - "wireless capacity and admission control in cognitive radio"
    - "wireless connectivity and capacity"

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.NI(Networking and Internet Architecture)