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 "complexity of urllc scheduling and efficient approximation schemes"):
  Abstract: In this paper we address the problem of joint admission control and resource scheduling for \emph{Ultra Reliable Low Latency Communications} (URLLC). We examine two models: (i) the \emph{continuous}, where all allocated resource blocks contribute to the success probability, and (ii) a \emph{binary}, where only resource blocks with strong signal are "active" for each user, and user $k$ needs $d_k$ active resource blocks for a successful URLLC transmission. In situations of congestion, we are interested in finding a subset of users that can be scheduled simultaneously. We show that finding a feasible schedule for at least $m$ URLLC users is NP-complete in the (easier) binary SNR model, hence also in the continuous. Maximizing the reward obtained from a feasible set of URLLC users is NP-hard and inapproximable to within ${(\log_2d)^2}/{d}$ of the optimal, where $d\doteq \max_kd_k$. On the other hand, we prove that checking a candidate set of users for feasibility and finding the corresponding schedule (when feasible) can be done in polynomial time, which we exploit to design an efficient heuristic algorithm for the general continuous SNR model. We complement our theoretical contributions with a numerical evaluation of our proposed schemes.

Includes members:
  Member "resource allocation and harq optimization for urllc traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "joint uplink and downlink resource configuration for ultra reliable and low latency communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random access based reliable uplink communication and power transfer using dynamic power splitting")
  Member "an examination of the benefits of scalable tti for heterogeneous traffic management in 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource optimization with flexible numerology and frame structure for heterogeneous services")
  Member "network slicing based 5g and future mobile networks mobility resource management and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "joint scheduling of urllc and embb traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "delay performance of miso wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "massive mimo for internet of things iot connectivity")

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)