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: "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.

Connected through hyperedge HE-1 (induced by source paper "intrinsic entropies of log concave distributions"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"

Connected through hyperedge HE-2 (induced by source paper "capacity of the two hop relay channel with wireless energy transfer from relay to source and energy transmission cost"):
  with members:
    - "throughput analysis and optimization of wireless powered multiple antenna full duplex relay systems"
    - "wireless powered communication opportunities and challenges"
    - "rate maximization of decode and forward relaying systems with rf energy harvesting"
    - "optimal cooperative power allocation for energy harvesting enabled relay networks"
    - "wireless powered cooperative communications via a hybrid relay"
    - "multiple access fading channel with wireless power transfer and energy harvesting"
    - "power allocation strategies in energy harvesting wireless cooperative networks"
    - "power allocation for conventional and buffer aided link adaptive relaying systems with energy harvesting nodes"

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)