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: "embb urllc resource slicing a risk sensitive approach"
  Abstract: Ultra Reliable Low Latency Communication (URLLC) is a 5G New Radio (NR) application that requires strict reliability and latency. URLLC traffic is usually scheduled on top of the ongoing enhanced Mobile Broadband (eMBB) transmissions ( i.e.,  puncturing the current eMBB transmission) and cannot be queued due to its hard latency requirements. In this letter, we propose a risk-sensitive based formulation to allocate resources to the incoming URLLC traffic, while minimizing the risk of the eMBB transmission ( i.e.,  protecting the eMBB users with low data rate) and ensuring URLLC reliability. Specifically, the Conditional Value at Risk (CVaR) is introduced as a risk measure for eMBB transmission. Moreover, the reliability constraint of URLLC is formulated as a chance constraint and relaxed based on Markov’s inequality. We decompose the formulated problem into two subproblems in order to transform it into a convex form and then alternatively solve them until convergence. Simulation results show that the proposed approach allocates resources to the incoming URLLC traffic efficiently, while satisfying the reliability of both eMBB and URLLC.

Connected through hyperedge HE-1 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "coexistence mechanism between embb and urllc in 5g wireless networks"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "coexistence of urllc and embb services in the c ran uplink an information theoretic study"
    - "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"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"

Connected through hyperedge HE-2 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "wireless network intelligence at the edge"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "channel charting locating users within the radio environment using channel state information"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "adam a method for stochastic optimization"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-3 (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"
    - "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"
    - "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"
    - "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-4 (induced by source paper "coexistence mechanism between embb and urllc in 5g wireless networks"):
  with members:
    - "coexistence of urllc and embb services in the c ran uplink an information theoretic study"
    - "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"
    - "network slicing for service oriented networks under resource constraints"
    - "joint scheduling of urllc and embb traffic in 5g wireless networks"
    - "resource scheduling for mixed traffic types with scalable tti in dynamic tdd systems"
    - "the dispersion of nearest neighbor decoding for additive non gaussian channels"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-5 (induced by source paper "how should i orchestrate resources of my slices for bursty urllc service provision"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"
    - "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-6 (induced by source paper "ran slicing in a uav network for embb service provision"):
  with members:
    - "a utility driven multi queue admission control solution for network slicing"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "a lyapunov optimization approach to repeated stochastic games"

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.NI(Networking and Internet Architecture)