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: "topology discovery of sparse random graphs with few participants"
  Abstract: We consider the task of topology discovery of sparse random graphs using end-to-end random measurements (e.g., delay) between a subset of nodes, referred to as the participants. The rest of the nodes are hidden, and do not provide any information for topology discovery. We consider topology discovery under two routing models: (a) the participants exchange messages along the shortest paths and obtain end-to-end measurements, and (b) additionally, the participants exchange messages along the second shortest path. For scenario (a), our proposed algorithm results in a sub-linear edit-distance guarantee using a sub-linear number of uniformly selected participants. For scenario (b), we obtain a much stronger result, and show that we can achieve consistent reconstruction when a sub-linear number of uniformly selected nodes participate. This implies that accurate discovery of sparse random graphs is tractable using an extremely small number of participants. We finally obtain a lower bound on the number of participants required by any algorithm to reconstruct the original random graph up to a given edit distance. We also demonstrate that while consistent discovery is tractable for sparse random graphs using a small number of participants, in general, there are graphs which cannot be discovered by any algorithm even with a significant number of participants, and with the availability of end-to-end information along all the paths between the participants.

Connected through hyperedge HE-1 (induced by source paper "reconstruction of c c channel for structured p2p botnet"):
  with members:
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "defense strategies against modern botnets"

Connected through hyperedge HE-2 (induced by source paper "topology discovery using path interference"):
  with members:
    - "active learning of multiple source multiple destination topologies"

Connected through hyperedge HE-3 (induced by source paper "occam an optimization based approach to network inference"):
  with members:
    - "dimes let the internet measure itself"
    - "efficient algorithms for large scale topology discovery"

Connected through hyperedge HE-4 (induced by source paper "submodular variational inference for network reconstruction"):
  with members:
    - "polyhedral aspects of submodularity convexity and concavity"
    - "seeing the unseen network inferring hidden social ties from respondent driven sampling"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "fast semidifferential based submodular function optimization"
    - "submodular inference of diffusion networks from multiple trees"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-5 (induced by source paper "learning graphical models from the glauber dynamics"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "maximum likelihood bounded tree width markov networks"
    - "on learning discrete graphical models using greedy methods"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "reconstruction of markov random fields from samples some easy observations and algorithms"

Connected through hyperedge HE-6 (induced by source paper "near linear query complexity for graph inference"):
  with members:
    - "graph reconstruction via distance oracles"
    - "efficient measurement of complex networks using link queries"
    - "exploring networks with traceroute like probes theory and simulations"

Connected through hyperedge HE-7 (induced by source paper "active learning of multiple source multiple destination topologies"):
  with members:
    - "active topology inference using network coding"
    - "network tomography based on additive metrics"

Connected through hyperedge HE-8 (induced by source paper "active topology inference using network coding"):
  with members:
    - "a network coding approach to loss tomography"
    - "passive network tomography for erroneous networks a network coding approach"
    - "compressive sensing over graphs"
    - "graph constrained group testing"
    - "detection understanding and prevention of traceroute measurement artifacts"
    - "dimes let the internet measure itself"

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.SI(Social and Information Networks)