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: "passive network tomography for erroneous networks a network coding approach"
  Abstract: Passive network tomography uses end-to-end observations of network communications to characterize the network, for instance, to estimate the network topology and to localize random or adversarial faults. Under the setting of linear network coding, this work provides a comprehensive study of passive network tomography in the presence of network (random or adversarial) faults. To be concrete, this work is developed along two directions: 1) tomographic upper and lower bounds (i.e., the most adverse conditions in each problem setting under which network tomography is possible, and corresponding schemes (computationally efficient, if possible) that achieve this performance) are presented for random linear network coding (RLNC). We consider RLNC designed with common randomness, i.e., the receiver knows the random codebooks of all intermediate nodes. (To justify this, we show an upper bound for the problem of topology estimation in networks using RLNC without common randomness.) In this setting, we present the first set of algorithms that characterize the network topology exactly. Our algorithm for topology estimation with random network errors has time complexity that is polynomial in network parameters. For the problem of network error localization given the topology information, we present the first computationally tractable algorithm to localize random errors, and prove that it is computationally intractable to localize adversarial errors. 2) New network coding schemes are designed that improve the tomographic performance of RLNC while maintaining the desirable low-complexity, throughput-optimal, distributed linear network coding properties of RLNC. In particular, we design network codes based on Reed–Solomon codes so that a maximal number of adversarial errors can be localized in a computationally efficient manner even without the information of network topology. The tomography schemes proposed in the paper can be used to monitor networks with other faults such as packet losses and link delays, etc.

Connected through hyperedge HE-1 (induced by source paper "a survey on network tomography with network coding"):
  with members:
    - "active topology inference using network coding"
    - "dce a novel delay correlation measurement for tomography with passive realization"
    - "subspace properties of network coding and their applications"
    - "a survey on network codes for distributed storage"

Connected through hyperedge HE-2 (induced by source paper "active topology inference using network coding"):
  with members:
    - "a network coding approach to loss tomography"
    - "topology discovery of sparse random graphs with few participants"
    - "compressive sensing over graphs"
    - "graph constrained group testing"
    - "detection understanding and prevention of traceroute measurement artifacts"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-3 (induced by source paper "dce a novel delay correlation measurement for tomography with passive realization"):
  with members:
    - "a survey on network tomography with network coding"

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)