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: "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.

Connected through hyperedge HE-1 (induced by source paper "learn2mac online learning multiple access for urllc applications"):
  with members:
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "irregular repetition slotted aloha over the rayleigh block fading channel with capture"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in 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.IT(Information Theory)