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: "consistency in the face of change an adaptive approach to physical layer cooperation"
  Abstract: Most existing works on physical-layer (PHY) cooperation (beyond routing) focus on how to best use a given, static relay network--while wireless networks are anything but static. In this paper, we pose a different set of questions: given that we have multiple devices within range, which relay(s) do we use for PHY cooperation, to maintain a consistent target performance? How can we efficiently adapt, as network conditions change? And how important is it, in terms of performance, to adapt? Although adapting to the best path when routing is a well understood problem, how to do so over PHY cooperation networks is an open question. Our contributions are: (1) We demonstrate via theoretical evaluation, a diminishing returns trend as the number of deployed relays increases. (2) Using a simple algorithm based on network metrics, we efficiently select the sub-network to use at any given time to maintain a target reliability. (3) When streaming video from Netflix, we experimentally show (using measurements from a WARP radio testbed employing DIQIF relaying) that our adaptive PHY cooperation scheme provides a throughput gain of 2x over nonadaptive PHY schemes, and a gain of 6x over genie-aided IP-level adaptive routing.

Connected through hyperedge HE-1 (induced by source paper "wireless network simplification the performance of routing"):
  with members:
    - "network simplification in half duplex building on submodularity"
    - "network simplification for secure af relaying"
    - "cut set bound is loose for gaussian relay networks"
    - "wireless network information flow a deterministic approach"
    - "wireless network simplification the gaussian n relay diamond network"
    - "a simple cooperative diversity method based on network path selection"

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)