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 "identifying randomly activated users via sign compute resolve on graphs"):
  Abstract: In this paper we treat the problem of identification of a subset of active users in a set of a large number of potentially active users. The users from the subset are activated randomly, such that the access point (AP) does not know the subset or its size a priori. The active users are contending to report their activity to the AP over a multiple access channel. We devise a contention algorithm that assumes a combination of physical-layer network coding and K-out-of-N signature coding, allowing for multiple detection of up to K users at the access point. In addition, we rely on the principles of coded slotted ALOHA (CSA) and use of successive interference cancellation to enable subsequent resolution of the collisions that originally featured more than K users. The objective is to identify the subset of active users such that the target performance, e.g., probability of active user resolution and/or throughput is reached, which implies that the duration of the contention period is also not known a priori. In contrast to standard CSA approaches, in the proposed algorithm each user, active or not, has a predefined schedule of slots in which it sends its signature. We analyze the performance of the proposed algorithm both in the asymptotic and non-asymptotic settings. We also derive an estimator that, based on the observation of collision multiplicities, estimates how many users are active and thereby enables tuning of the length of the contention period.

Includes members:
  Member "a pseudo bayesian approach to sign compute resolve slotted aloha":
    also connected through other hyperedges:
      (none)
  Member "coded slotted aloha a graph based method for uncoordinated multiple access":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo")
  Member "coded random access applying codes on graphs to design random access protocols":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "coded random access how coding theory helps to build random access protocols":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wireless access in ultra reliable low latency communication urllc")
  Member "sign compute resolve for random access":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural network aided decoding for physical layer network coding random access")
  Member "aloha random access that operates as a rateless code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online estimation and adaptation for random access with successive interference cancellation")
  Member "bounded contention coding for wireless networks in the high snr regime":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross sender bit mixing coding")
  Member "frameless aloha protocol for wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reliability latency performance of frameless aloha with and without feedback")

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)