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 "random access channel coding in the finite blocklength regime"):
  Abstract: The paper considers a random multiple access channel communication scenario with an unknown number of transmitters. The collection of active transmitters remains fixed during each epoch, but neither the transmitters nor the receiver know which subset of transmitters that is nor even the number of active transmitters. The receiver discerns from the channel output both the number of active transmitters ($k$) and their messages. The receiver takes more time to decode if the number of active transmitters is high, and it uses 1-bit feedback at fixed transmission times to inform all transmitters when it is ready to accept a new transmission. For a class of permutation-invariant channels, the central result of this work demonstrates the achievability of performance that is first-order optimal for the Multiple Access Channel (MAC) in operation during each coding epoch. In that class, the proposed scheme achieves the same dispersion using a single threshold rule as that achieved by prior multiple access schemes requiring $2^{k}-1$ simultaneous threshold rules and a fixed number of transmitters.

Includes members:
  Member "lossless source coding in the point to point multiple access and random access scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "capacity of gaussian many access channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "a proof of the strong converse theorem for gaussian multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "many access channels the gaussian case with random user activities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "aloha random access that operates as a rateless code":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online estimation and adaptation for random access with successive interference cancellation")
  Member "second order rate region of constant composition codes for the multiple access channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "random access an information theoretic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interference prediction in wireless networks stochastic geometry meets recursive filtering")

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)