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: "link delay estimation via expander graphs"
  Abstract: One of the purposes of network tomography is to infer the status of parameters (e.g., delay) for the links inside a network through end-to-end probing between (external) boundary nodes along predetermined routes. In this work, we apply concepts from compressed sensing and expander graphs to the delay estimation problem. We first show that a relative majority of network topologies are not expanders for existing expansion criteria. Motivated by this challenge, we then relax such criteria, enabling us to acquire simulation evidence that link delays can be estimated for 30% more networks. That is, our relaxation expands the list of identifiable networks with bounded estimation error by 30%. We conduct a simulation performance analysis of delay estimation and congestion detection on the basis of l1 minimization, demonstrating that accurate estimation is feasible for an increasing proportion of networks.

Connected through hyperedge HE-1 (induced by source paper "link delay estimation using sparse recovery for dynamic network tomography"):
  with members:
    - "compressive sensing over graphs"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing in multi hop large scale wireless sensor networks based on routing topology tomography"):
  with members:
    - "discovering the structure of complex networks by minimizing cyclic bandwidth sum"
    - "heuristic for solving cyclic bandwidth sum problem by following the structure of the graph"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-3 (induced by source paper "reflective network tomography based on compressed sensing"):
  with members:
    - "synchronization free delay tomography based on compressed sensing"
    - "compressive sensing over graphs"

Connected through hyperedge HE-4 (induced by source paper "sparse recovery with graph constraints"):
  with members:
    - "theory and applications of compressed sensing"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "compressive sensing over graphs"
    - "graph constrained group testing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-5 (induced by source paper "sparse recovery with graph constraints fundamental limits and measurement construction"):
  with members:
    - "compressive sensing over graphs"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)