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 "fundamental limits of covert bit insertion in packets"):
  Abstract: Covert communication is necessary when revealing the mere existence of a message leaks sensitive information to an attacker. Consider a network link where an authorized transmitter Jack sends packets to an authorized receiver Steve, and the packets visit Alice, Willie, and Bob, respectively, before they reach Steve. Covert transmitter Alice wishes to alter the packet stream in some way to send information to covert receiver Bob without watchful and capable adversary Willie being able to detect the presence of the message. In our previous works, we addressed two techniques for such covert transmission from Alice to Bob: packet insertion and packet timing. In this paper, we consider covert communication via bit insertion in packets with available space (e.g., with size less than the maximum transmission unit). We consider three scenarios: 1) packet sizes are independent and identically distributed (i.i.d.) with a probability mass function (pmf) whose support is a set of one bit spaced values; 2) packet sizes are i.i.d. with a pmf whose support is arbitrary; 3) packet sizes may be dependent. For the first and second assumptions, we show that Alice can covertly insert $\mathcal{O}(\sqrt{n})$ bits of information in a flow of $n$ packets; conversely, if she inserts $\omega(\sqrt{n})$ bits of information, Willie can detect her with arbitrarily small error probability. For the third assumption, we prove Alice can covertly insert on average $\mathcal{O}(c(n)/\sqrt{n})$ bits in a sequence of $n$ packets, where $c(n)$ is the average number of conditional pmf of packet sizes given the history, with a support of at least size two.

Includes members:
  Member "first and second order asymptotics in covert communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keyless covert communication via channel state information")
  Member "asymptotic loss in privacy due to dependency in gaussian traces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic limits of privacy in bayesian time series matching")
  Member "embedding covert information in broadcast communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "undetectable radios covert communication under spectral mask constraints")
  Member "fundamental limits of invisible flow fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamental limits of covert packet insertion")
  Member "privacy preserving mhealth data release with pattern consistency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chasing accuracy and privacy and catching both a literature survey on differentially private histogram publication")
  Member "privacy against statistical matching inter user correlation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotic limits of privacy in bayesian time series matching")
  Member "towards provably invisible network flow fingerprints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fundamental limits of covert packet insertion")
  Member "matching anonymized and obfuscated time series to users profiles":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")

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)