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: "risk aware resource allocation for urllc challenges and strategies with machine learning"
  Abstract: Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer solutions have been investigated to satisfy only scheduled URLLC traffic, there is a lack of study on enabling transmission of non-scheduled URLLC traffic, especially in coexistence with the scheduled URLLC traffic. Machine learning (ML) is an important enabler for such a co-existence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. Hence, in this paper, we first study the coexistence design challenges, especially the radio resource management (RRM) problem and propose a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each URLLC traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the benefits of leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99% reliability of both scheduled and nonscheduled traffic types is satisfied.

Connected through hyperedge HE-1 (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"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "deep learning for encrypted traffic classification an overview"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

Connected through hyperedge HE-5 (induced by source paper "dynamic multi connectivity activation for ultra reliable and low latency communication"):
  with members:
    - "on the resource utilization of multi connectivity transmission for urllc services in 5g new radio"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"

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)