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: "effective capacity and power allocation for machine type communication"
  Abstract: Effective capacity (EC) determines the maximum communication rate subject to a particular delay constraint. In this work, we analyze the EC of ultra reliable Machine Type Communication (MTC) networks operating in the finite blocklength (FB) regime. First, we present a closed form approximation for EC in quasi-static Rayleigh fading channels. Our analysis determines the upper bounds for EC and delay constraint when varying transmission power. Finally, we characterize the power-delay trade-off for fixed EC and propose an optimum power allocation scheme which exploits the asymptotic behavior of EC in the high SNR regime. The results illustrate that the proposed scheme provides significant power saving with a negligible loss in EC.

Connected through hyperedge HE-1 (induced by source paper "ultra reliable communication via optimum power allocation for harq retransmission schemes"):
  with members:
    - "low latency short packet transmissions fixed length or harq"
    - "on the effective energy efficiency of ultra reliable networks in the finite blocklength regime"
    - "ultra reliable communication via optimum power allocation for type i arq in finite block length"
    - "short packet communications over multiple antenna rayleigh fading channels"
    - "energy efficient adaptive power allocation for incremental mimo systems"
    - "green communication via type i arq finite block length analysis"
    - "finite block length analysis of the incremental redundancy harq"
    - "ultra reliable communication in 5g wireless systems"

Connected through hyperedge HE-2 (induced by source paper "effective capacity of noma with finite blocklength for low latency communications"):
  with members:
    - "on the performance of non orthogonal multiple access in the finite blocklength regime"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "performance analysis of noma for ultra reliable and low latency communications"
    - "effective capacity in wireless networks a comprehensive survey"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"

Connected through hyperedge HE-3 (induced by source paper "noma versus oma in finite blocklength regime link layer rate performance"):
  with members:
    - "on the performance of non orthogonal multiple access in the finite blocklength regime"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "performance analysis of noma for ultra reliable and low latency communications"
    - "effective capacity in wireless networks a comprehensive survey"

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)