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: "energy efficient ofdma networks maintaining statistical qos guarantees for delay sensitive traffic"
  Abstract: An energy-efficient design is proposed under specific statistical quality-of-service (QoS) guarantees for delay-sensitive traffic in the downlink orthogonal frequency-division multiple-access networks. This design is based on Wu’s effective capacity (EC) concept, which characterizes the maximum throughput of a system subject to statistical delay-QoS requirements at the data-link layer. In the particular context considered, our main contributions consist of quantifying the effective energy-efficiency (EEE)-versus-EC tradeoff and characterizing the delay-sensitive traffic as a function of the QoS-exponent    $\theta $   , which expresses the exponential decay rate of the delay-QoS violation probabilities. Upon exploiting the properties of fractional programming, the originally quasi-concave EEE optimization problem having a fractional form is transformed into a subtractive optimization problem by applying Dinkelbach’s method. As a result, an iterative inner–outer loop-based resource allocation algorithm is conceived for efficiently solving the transformed EEE optimization problem. Our simulation results demonstrate that the proposed scheme converges within a few Dinkelbach algorithm’s iterations to the desired solution accuracy. Furthermore, the impact of the circuitry power, the QoS-exponent, and the power amplifier inefficiency is characterized numerically. These results reveal that the optimally allocated power maximizing the EEE decays exponentially with respect to both the circuitry power and the QoS-exponent, while decaying linearly with respect to the power amplifier inefficiency.

Connected through hyperedge HE-1 (induced by source paper "effective capacity in wireless networks a comprehensive survey"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "performance analysis of noma for ultra reliable and low latency communications"
    - "wireless access for ultra reliable low latency communication urllc principles and building blocks"
    - "secure transmission of delay sensitive data over wireless fading channels"
    - "statistical qos analysis of full duplex and half duplex heterogeneous cellular networks"
    - "effective capacity of retransmission schemes a recurrence relation approach"
    - "backlog and delay reasoning in harq systems"
    - "delay constrained video transmission quality driven resource allocation and scheduling"

Connected through hyperedge HE-2 (induced by source paper "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"):
  with members:
    - "distributed energy spectral efficiency optimization for partial full interference alignment in multi user multi relay multi cell mimo systems"
    - "wireless sensor network virtualization a survey"
    - "markov decision processes with applications in wireless sensor networks a survey"
    - "accuracy of homology based coverage hole detection for wireless sensor networks on sphere"
    - "end to end reliability aware scheduling for wireless sensor networks"
    - "multiband spectrum access great promises for future cognitive radio networks"
    - "spectral and energy spectral efficiency optimization of joint transmit and receive beamforming based multi relay mimo ofdma cellular networks"
    - "achieving maximum energy efficiency in multi relay ofdma cellular networks a fractional programming approach"

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)