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 "a finite block length achievability bound for low probability of detection communication"):
  Abstract: Low probability of detection (or covert) communication refers to the scenario where information must be sent reliably to a receiver, but with low probability of detection by an adversary. Recent works on the fundamental limits of this communication problem have established achievability and converse bounds that are asymptotic in the block length of the code. This paper uses Gallager's random coding bound to derive a new achievability bound that is applicable to low probability of detection communication in the finite block length regime. Further insights are unveiled that are otherwise hidden in previous asymptotic analyses.

Includes members:
  Member "covert communication with finite blocklength in awgn channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "covert transmission with a self sustained relay")
  Member "fundamental limits of communication with low probability of detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "keyless covert communication via channel state information")
  Member "covert communication over noisy channels a resolvability perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "keyless covert communication via channel state information")
  Member "limits of reliable communication with low probability of detection on awgn channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "keyless covert communication via channel state information")
  Member "simple channel coding bounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the conditional smooth renyi entropy and its applications in guessing and source coding")

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)