human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale"):
  Abstract: Ensuring ultra-reliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture, and topology (across access, edge, and core) and decision-making under uncertainty is sorely lacking. The overarching goal of this article is a first step to fill this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a plethora of techniques and methodologies pertaining to the requirements of ultra-reliable and low-latency communication, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliable wireless networks.

Includes members:
  Member "ultra reliable communication in 5g mmwave networks a risk sensitive approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "federated learning for ultra reliable low latency v2v communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "ultra reliable and low latency vehicular transmission an extreme value theory approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "network slicing for ultra reliable low latency communication in industry 4 0 scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance analysis of ultra reliable short message decode and forward relaying protocols")
  Member "proactive edge computing in latency constrained fog networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "fine grained reliability for v2v communications around suburban and urban intersections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance analysis of cooperative communications at road intersections using stochastic geometry tools")
  Member "introduction to ultra reliable and low latency communications in 5g":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on low latency towards 5g ran core network and caching solutions")

Question: Please tell me which class the source paper 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)