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: "on achievable rates of awgn energy harvesting channels with block energy arrival and non vanishing error probabilities"
  Abstract: This paper investigates the achievable rates of an additive white Gaussian noise (AWGN) energy-harvesting (EH) channel with an infinite battery under the assumption that the error probabilities do not vanish as the blocklength increases. The EH process is characterized by a sequence of blocks of harvested energy. The harvested energy remains constant within a block while the harvested energy across different blocks is characterized by a sequence of independent and identically distributed (i.i.d.) random variables. The blocks have length $L$, which can be interpreted as the coherence time of the energy arrival process. If $L$ is a constant or grows sublinearly in the blocklength $n$, we fully characterize the first-order coding rate. In addition, we obtain lower and upper bounds on the second-order coding rate, which are proportional to $-\sqrt{\frac{L}{n}}$ for any fixed error probability $<1/2$. If $L$ grows linearly in $n$, we obtain lower and upper bounds on the first-order coding rate, which coincide whenever the EH random variable is continuous. Our results suggest that correlation in the energy-arrival process decreases the effective blocklength by a factor of~$L$.

Connected through hyperedge HE-1 (induced by source paper "second order and moderate deviation analysis of a block fading channel with deterministic and energy harvesting power constraints"):
  with members:
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "beta beta bounds finite blocklength analog of the golden formula"
    - "finite blocklength achievable rates for energy harvesting awgn channels with infinite buffer"
    - "non asymptotic achievable rates for energy harvesting channels using save and transmit"
    - "short packet communications over multiple antenna rayleigh fading channels"
    - "second order coding rates for channels with state"
    - "optimum power control at finite blocklength"

Connected through hyperedge HE-2 (induced by source paper "non asymptotic achievable rates for gaussian energy harvesting channels best effort and save and transmit"):
  with members:
    - "online power control for block i i d energy harvesting channels"
    - "capacity analysis of discrete energy harvesting channels"
    - "finite blocklength achievable rates for energy harvesting awgn channels with infinite buffer"
    - "non asymptotic achievable rates for energy harvesting channels using save and transmit"
    - "capacity of the energy harvesting channel with a finite battery"
    - "closed form delay optimal power control for energy harvesting wireless system with finite energy storage"
    - "the binary energy harvesting channel with a unit sized battery"

Connected through hyperedge HE-3 (induced by source paper "online power control for block i i d energy harvesting channels"):
  with members:
    - "communication over a time correlated channel with an energy harvesting transmitter"
    - "capacity analysis of discrete energy harvesting channels"
    - "capacity of the energy harvesting gaussian mac"
    - "capacity of the energy harvesting channel with a finite battery"
    - "universally near optimal online power control for energy harvesting nodes"
    - "closed form delay optimal power control for energy harvesting wireless system with finite energy storage"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "on online energy harvesting in multiple access communication systems"

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)