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 "optimal discrete power control in poisson clustered ad hoc networks"):
  Abstract: Power control in a digital handset is practically implemented in a discrete fashion and usually such a discrete power control (DPC) scheme is suboptimal. In this paper, we first show that in a Poison-distributed ad hoc network, if DPC is properly designed with a certain condition satisfied, it can strictly work better than constant power control (i.e. no power control) in terms of average signal-to-interference ratio, outage probability and spatial reuse. This motivates us to propose an $N$-layer DPC scheme in a wireless clustered ad hoc network, where transmitters and their intended receivers in circular clusters are characterized by a Poisson cluster process (PCP) on the plane $\mathbb{R}^2$. The cluster of each transmitter is tessellated into $N$-layer annuli with transmit power $P_i$ adopted if the intended receiver is located at the $i$-th layer. Two performance metrics of transmission capacity (TC) and outage-free spatial reuse factor are redefined based on the $N$-layer DPC. The outage probability of each layer in a cluster is characterized and used to derive the optimal power scaling law $P_i=\Theta\left(\eta_i^{-\frac{\alpha}{2}}\right)$, with $\eta_i$ the probability of selecting power $P_i$ and $\alpha$ the path loss exponent. Moreover, the specific design approaches to optimize $P_i$ and $N$ based on $\eta_i$ are also discussed. Simulation results indicate that the proposed optimal $N$-layer DPC significantly outperforms other existing power control schemes in terms of TC and spatial reuse.

Includes members:
  Member "multicast outage probability and transmission capacity of multihop wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a general mimo framework for noma downlink and uplink transmission based on signal alignment")
  Member "an overview of the transmission capacity of wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")
  Member "interference and outage in clustered wireless ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")
  Member "fractional power control for decentralized wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stability and metastability of traffic dynamics in uplink random access networks")
  Member "the effect of fading channel inversion and threshold scheduling on ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hiding communications in awgn channels and thz band with interference uncertainty")

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.IT(Information Theory)