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: "quantum noise limited communication with low probability of detection"
  Abstract: 0. We analyze the thermal noise and pure loss channels. The square root law holds for the thermal noise channel even if Willie employs a quantum-optimal measurement, while Bob is equipped with a standard coherent detection receiver. We also show that LPD communication is not possible on the pure loss channel. However, this result assumes Willie to possess an ideal receiver that is not subject to excess noise. If Willie is restricted to a practical receiver with a non-zero dark current, the square root law is achievable on the pure loss channel. Typically wireless data transmission is secured from an eavesdropping third party by a cryptographic encryption pro- tocol. However, there are real-life scenarios where encryp- tion arouses suspicion and even theoretically robust encryp- tion can be defeated by a determined adversary using a non- computational method such as side-channel analysis. Thus, protection from interception is often insufficient and the ad- versary's ability to even detect the presence of a transmission must be limited. This is known as low probability of detection

Connected through hyperedge HE-1 (induced by source paper "covert communications on poisson packet channels"):
  with members:
    - "covert single hop communication in a wireless network with distributed artificial noise generation"
    - "reliable deniable and hidable communication over multipath networks"
    - "lpd communication when the warden does not know when"
    - "reliable deniable communication hiding messages in noise"
    - "limits of reliable communication with low probability of detection on awgn channels"
    - "effective secrecy reliability confusion and stealth"

Connected through hyperedge HE-2 (induced by source paper "covert communication gains from adversary s ignorance of transmission time"):
  with members:
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "quantum secure covert communication on bosonic channels"
    - "covert single hop communication in a wireless network with distributed artificial noise generation"
    - "reliable deniable and hidable communication over multipath networks"
    - "lpd communication when the warden does not know when"
    - "reliable deniable communication hiding messages in noise"
    - "limits of reliable communication with low probability of detection on awgn channels"

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)