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 "a geometric analysis of the awgn channel with a sigma rho power constraint"):
  Abstract: In this paper, we consider the AWGN channel with a power constraint called the $(\sigma, \rho)$-power constraint, which is motivated by energy harvesting communication systems. Given a codeword, the constraint imposes a limit of $\sigma + k \rho$ on the total power of any $k\geq 1$ consecutive transmitted symbols. Such a channel has infinite memory and evaluating its exact capacity is a difficult task. Consequently, we establish an $n$-letter capacity expression and seek bounds for the same. We obtain a lower bound on capacity by considering the volume of ${\cal S}_n(\sigma, \rho) \subseteq \mathbb{R}^n$, which is the set of all length $n$ sequences satisfying the $(\sigma, \rho)$-power constraints. For a noise power of $\nu$, we obtain an upper bound on capacity by considering the volume of ${\cal S}_n(\sigma, \rho) \oplus B_n(\sqrt{n\nu})$, which is the Minkowski sum of ${\cal S}_n(\sigma, \rho)$ and the $n$-dimensional Euclidean ball of radius $\sqrt{n\nu}$. We analyze this bound using a result from convex geometry known as Steiner's formula, which gives the volume of this Minkowski sum in terms of the intrinsic volumes of ${\cal S}_n(\sigma, \rho)$. We show that as the dimension $n$ increases, the logarithm of the sequence of intrinsic volumes of $\{{\cal S}_n(\sigma, \rho)\}$ converges to a limit function under an appropriate scaling. The upper bound on capacity is then expressed in terms of this limit function. We derive the asymptotic capacity in the low and high noise regime for the $(\sigma, \rho)$-power constrained AWGN channel, with strengthened results for the special case of $\sigma = 0$, which is the amplitude constrained AWGN channel.

Includes members:
  Member "near optimal energy control and approximate capacity of energy harvesting communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a maximin optimal online power control policy for energy harvesting communications")
  Member "binary energy harvesting channel with finite energy storage":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint transfer of energy and information in a two hop relay channel")

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)