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: "wireless edge computing with latency and reliability guarantees"
  Abstract: Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.

Connected through hyperedge HE-1 (induced by source paper "adaptive task partitioning at local device or remote edge server for offloading in mec"):
  with members:
    - "towards 5g enabled tactile robotic telesurgery"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication"):
  with members:
    - "vehicular edge computing via deep reinforcement learning"
    - "learning to optimize training deep neural networks for interference management"
    - "federated learning for ultra reliable low latency v2v communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "user association and load balancing for massive mimo through deep learning"
    - "load balancing user association in millimeter wave mimo networks"
    - "reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks"

Connected through hyperedge HE-3 (induced by source paper "federated learning for edge networks resource optimization and incentive mechanism"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated multi task learning"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "communication efficient learning of deep networks from decentralized data"

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)