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: "geographic routing with limited information in sensor networks"
  Abstract: Geographic routing with greedy relaying strategies have been widely studied as a routing scheme in sensor networks. These schemes assume that the nodes have perfect information about the location of the destination. When the distance between the source and destination is normalized to unity, the asymptotic routing delays in these schemes are $\Theta(\frac{1}{M(n)}),$ where M(n) is the maximum distance traveled in a single hop (transmission range of a radio). In this paper, we consider routing scenarios where nodes have location errors (imprecise GPS), or where only coarse geographic information about the destination is available, and only a fraction of the nodes have routing information. We show that even with such imprecise or limited destination-location information, the routing delays are $\Theta(\frac{1}{M(n)})$. We also consider the throughput-capacity of networks with progressive routing strategies that take packets closer to the destination in every step, but not necessarily along a straight-line. We show that the throughput-capacity with progressive routing is order-wise the same as the maximum achievable throughput-capacity.

Connected through hyperedge HE-1 (induced by source paper "large overlaid cognitive radio networks from throughput scaling to asymptotic multiplexing gain"):
  with members:
    - "on asymptotic statistics for geometric routing schemes in wireless ad hoc networks"
    - "on the random 1 2 disk routing scheme in wireless ad hoc networks"
    - "cognitive networks achieve throughput scaling of a homogeneous network"
    - "an overview of the transmission capacity of wireless networks"
    - "scaling laws of cognitive 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.IT(Information Theory)