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 "ieee 802 15 4 e tsch based scheduling for throughput optimization a combinatorial multi armed bandit approach"):
  Abstract: In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.

Includes members:
  Member "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral efficiency analysis of cell free massive mimo systems with zero forcing detector")
  Member "online learning for combinatorial network optimization with restless markovian rewards":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online influence maximization in non stationary social networks")

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)