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: "fundamental limits of invisible flow fingerprinting"
  Abstract: Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections. Assume Alice and Bob have access to the input and the output links of an anonymous network, respectively, and they wish to collaboratively reveal the connections between the input and the output links without being detected by Willie who protects the network. Alice generates a codebook of fingerprints, where each fingerprint corresponds to a unique sequence of inter-packet delays and shares it only with Bob. For each input flow, she selects a fingerprint from the codebook and embeds it in the flow, i.e., changes the packet timings of the flow to follow the packet timings suggested by the fingerprint, and Bob extracts the fingerprints from the output flows. We model the network as parallel $M/M/1$ queues where each queue is shared by a flow from Alice to Bob and other flows independent of the flow from Alice to Bob. The timings of the flows are governed by independent Poisson point processes. Assuming all input flows have equal rates and that Bob observes only flows with fingerprints, we first present two scenarios: 1) Alice fingerprints all the flows; 2) Alice fingerprints a subset of the flows, unknown to Willie. Then, we extend the construction and analysis to the case where flow rates are arbitrary as well as the case where not all the flows that Bob observes have a fingerprint. For each scenario, we derive the number of flows that Alice can fingerprint and Bob can trace by fingerprinting.

Connected through hyperedge HE-1 (induced by source paper "fundamental limits of covert packet insertion"):
  with members:
    - "covert communication over a k user multiple access channel"
    - "fundamental limits of covert bit insertion in packets"
    - "privacy preserving mhealth data release with pattern consistency"
    - "covert capacity of non coherent rayleigh fading channels"
    - "towards provably invisible network flow fingerprints"
    - "covert wireless communication with artificial noise generation"
    - "covert communication over noisy channels a resolvability perspective"
    - "covert communications on renewal packet channels"

Connected through hyperedge HE-2 (induced by source paper "asymptotic limits of privacy in bayesian time series matching"):
  with members:
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "privacy of dependent users against statistical matching"
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"
    - "where you are is who you are user identification by matching statistics"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"

Connected through hyperedge HE-3 (induced by source paper "fundamental limits of covert bit insertion in packets"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "embedding covert information in broadcast communications"
    - "privacy preserving mhealth data release with pattern consistency"
    - "privacy against statistical matching inter user correlation"
    - "towards provably invisible network flow fingerprints"
    - "matching anonymized and obfuscated time series to users profiles"
    - "covert wireless communication with artificial noise generation"

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.NI(Networking and Internet Architecture)