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: "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"
  Abstract: This paper considers the coexistence of Ultra Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) services in the uplink of Cloud Radio Access Network (C-RAN) architecture based on the relaying of radio signals over analog fronthaul links. While Orthogonal Multiple Access (OMA) to the radio resources enables the isolation and the separate design of different 5G services, Non-Orthogonal Multiple Access (NOMA) can enhance the system performance by sharing wireless and fronthaul resources. This paper provides an information-theoretic perspective in the performance of URLLC and eMBB traffic under both OMA and NOMA. The analysis focuses on standard cellular models with additive Gaussian noise links and a finite inter-cell interference span, and it accounts for different decoding strategies such as puncturing, Treating Interference as Noise (TIN) and Successive Interference Cancellation (SIC). Numerical results demonstrate that, for the considered analog fronthauling C-RAN architecture, NOMA achieves higher eMBB rates with respect to OMA, while guaranteeing reliable low-rate URLLC communication with minimal access latency. Moreover, NOMA under SIC is seen to achieve the best performance, while, unlike the case with digital capacity-constrained fronthaul links, TIN always outperforms puncturing.

Connected through hyperedge HE-1 (induced by source paper "multicast embb and bursty urllc service multiplexing in a comp enabled ran"):
  with members:
    - "ran slicing for massive iot and bursty urllc service multiplexing analysis and optimization"
    - "embb urllc resource slicing a risk sensitive approach"
    - "harmonizing 3gpp and nfv description models providing customized ran slices in 5g networks"
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "sample approximation based deflation approaches for chance sinr constrained joint power and admission control"

Connected through hyperedge HE-2 (induced by source paper "how should i orchestrate resources of my slices for bursty urllc service provision"):
  with members:
    - "embb urllc resource slicing a risk sensitive approach"
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "network slicing games enabling customization in multi tenant networks"

Connected through hyperedge HE-3 (induced by source paper "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"):
  with members:
    - "joint power and time allocation for noma mec offloading"
    - "coexistence of urllc and embb services in the c ran uplink an information theoretic study"
    - "mixed delay constraints in wyner s soft handoff network"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "joint scheduling of urllc and embb traffic in 5g wireless networks"
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "fog computing based radio access networks issues and challenges"

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)