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 "unsupervised deep learning for ultra reliable and low latency communications"):
  Abstract: In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints. We take a joint power and bandwidth allocation problem as an example, which minimizes the total bandwidth required to guarantee the QoS of each user in terms of the delay bound and overall packet loss probability. The global optimal solution is found in a symmetric scenario. A neural network was introduced to find an approximated optimal solution in general scenarios, where the QoS is ensured by using the property that the optimal solution should satisfy as the "supervision signal". Simulation results show that the learning-based solution performs the same as the optimal solution in the symmetric scenario, and can save around 40% bandwidth with respect to the state-of-the-art policy.

Includes members:
  Member "fast harq over finite blocklength codes a technique for low latency reliable communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "short packet transmission via variable length codes in the presence of noisy stop feedback")
  Member "risk sensitive reinforcement learning for urllc traffic in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal learning for dynamic coding in deadline constrained multi channel networks")
  Member "federated learning for ultra reliable low latency v2v communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "joint uplink and downlink resource configuration for ultra reliable and low latency communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random access based reliable uplink communication and power transfer using dynamic power splitting")
  Member "reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "age of information for multicast transmission with fixed and random deadlines in iot systems")
  Member "realizing the tactile internet haptic communications over next generation 5g cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analyzing the tradeoffs in using millimeter wave directional links for high data rate tactile internet applications")
  Member "delay performance of wireless communications with imperfect csi and finite length coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noma in the uplink delay analysis with imperfect csi and finite length coding")
  Member "quasi static multiple antenna fading channels at finite blocklength":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust urllc packet scheduling of ofdm systems")

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.NI(Networking and Internet Architecture)