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 "multicast outage probability and transmission capacity of multihop wireless networks"):
  Abstract: Multicast transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include (i) identical packets must be delivered successfully to several nodes, (ii) outage at any receiver requires the packet to be retransmitted at least to that receiver, and (iii) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and retransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast transmission capacity (MTC) as the maximum achievable multicast rate per transmission attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into $v$ smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming $\tau/v$ transmission attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is $\Theta(\rho k^{x}\log(k)v^{y})$ where $\rho$, $x$ and $y$ are functions of $\tau$ and $v$ depending on the network size and intensity, and $k$ is the average number of the intended receivers in a cluster. We derive $\{\rho, x, y\}$ for a number of regimes of interest, and also show that an appropriate number of retransmissions can significantly enhance the MTC.

Includes members:
  Member "the balanced unicast and multicast capacity regions of large wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how much cache is needed to achieve linear capacity scaling in backhaul limited dense wireless networks")
  Member "spatial and temporal correlation of the interference in aloha ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatio temporal modeling for massive and sporadic access")
  Member "the effect of fading channel inversion and threshold scheduling on ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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)