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: "joint scheduling of urllc and embb traffic in 5g wireless networks"
  Abstract: Emerging 5G systems will need to efficiently support both broadband traffic (eMBB) and ultra-low-latency (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic. We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while satisfying instantaneous URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocations decisions of the eMBB scheduler. Next we consider a more general class of (convex) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but time-homogeneous policies. We validate the characteristics and benefits of our schedulers via simulation.

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"
    - "embb urllc resource slicing a risk sensitive approach"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "coexistence mechanism between embb and urllc in 5g wireless networks"):
  with members:
    - "embb urllc resource slicing a risk sensitive approach"
    - "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"
    - "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-3 (induced by source paper "scheduling observers over a shared channel with hard delivery deadlines"):
  with members:
    - "self driving cars a survey"
    - "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"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "cross layer optimization for ultra reliable and low latency radio access networks"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"

Connected through hyperedge HE-4 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint user association and resource allocation optimization for ultra reliable low latency hetnets"
    - "federated learning for ultra reliable low latency v2v communications"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"

Connected through hyperedge HE-5 (induced by source paper "complexity of urllc scheduling and efficient approximation schemes"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "an examination of the benefits of scalable tti for heterogeneous traffic management in 5g networks"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "delay performance of miso wireless communications"

Connected through hyperedge HE-6 (induced by source paper "embb urllc resource slicing a risk sensitive approach"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"

Connected through hyperedge HE-7 (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"
    - "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "fog computing based radio access networks issues and challenges"

Connected through hyperedge HE-8 (induced by source paper "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"):
  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"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "on the transport capability of lan cables in all analog mimo roc fronthaul"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "non orthogonal multiple access in multi cell networks theory performance and practical challenges"
    - "the dispersion of nearest neighbor decoding for additive non gaussian channels"
    - "ordered tomlinson harashima precoding in g fast downstream"

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)