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: "measurement based impromptu deployment of a multi hop wireless relay network"
  Abstract: We study the problem of optimal sequential ("as-you-go") deployment of wireless relay nodes as a person walks along a line of random length (with a known distribution). The objective is to create an impromptu multihop wireless network for connecting a packet source to be placed at the end of the line with a sink node located at the starting point, to operate in the light traffic regime. As the deployment person walks along the line from the sink towards the source, at every step, he measures the channel quality to one (or more) previously placed relays, and places the relay nodes based on these measurements, so as to minimize either the sum power or the maximum power from the source to the sink node in the resultant network, subject to a constraint on the expected number of relays placed. For each of these two objectives, two different relay selection strategies are considered: (i) each relay communicates with the sink via its immediate previous relay, (ii) the communication path can skip some of the deployed relays. With appropriate modeling assumptions, we formulate each of these problems as a Markov decision process (MDP). We provide the optimal policy structures for all these cases, and provide illustrations, via numerical results, for some typical parameters.

Connected through hyperedge HE-1 (induced by source paper "asynchronous stochastic approximation based learning algorithms for as you go deployment of wireless relay networks along a line"):
  with members:
    - "deploy as you go wireless relay placement an optimal sequential decision approach using the multi relay channel model"
    - "sequential decision algorithms for measurement based impromptu deployment of a wireless relay network along a line"
    - "impromptu deployment of wireless relay networks experiences along a forest trail"
    - "qos aware and survivable network design for planned wireless sensor networks"

Connected through hyperedge HE-2 (induced by source paper "deploy as you go wireless relay placement an optimal sequential decision approach using the multi relay channel model"):
  with members:
    - "optimal capacity relay node placement in a multi hop wireless network on a line"
    - "a robust relay placement framework for 60ghz mmwave wireless personal area networks"
    - "wireless network information flow a deterministic approach"
    - "optimal relay location and power allocation for low snr broadcast relay channels"

Connected through hyperedge HE-3 (induced by source paper "sequential decision algorithms for measurement based impromptu deployment of a wireless relay network along a line"):
  with members:
    - "impromptu deployment of wireless relay networks experiences along a forest trail"
    - "qos aware and survivable network design for planned wireless sensor networks"

Connected through hyperedge HE-4 (induced by source paper "impromptu deployment of wireless relay networks experiences along a forest trail"):
  with members:
    - "as you go deployment of a wireless network with on line measurements and backtracking"
    - "qos aware and survivable network design for planned wireless sensor networks"

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)