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: "active topology inference using network coding"
  Abstract: Abstract   Our goal, is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work in Fragouli et al. (2006). For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography in Rabbat et al. (2006), and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference,  traceroute , and packet marking.

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

Connected through hyperedge HE-2 (induced by source paper "active learning of multiple source multiple destination topologies"):
  with members:
    - "topology discovery of sparse random graphs with few participants"
    - "network tomography based on additive metrics"

Connected through hyperedge HE-3 (induced by source paper "learning the interference graph of a wireless network"):
  with members:
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "boolean compressed sensing and noisy group testing"
    - "distributed sensor failure detection in sensor networks"
    - "graph constrained group testing"

Connected through hyperedge HE-4 (induced by source paper "subspace properties of network coding and their applications"):
  with members:
    - "universal secure network coding via rank metric codes"
    - "communication over finite field matrix channels"
    - "understanding the properties of the bittorrent overlay"
    - "coding for errors and erasures in random network coding"
    - "minimum cost multicast over coded packet networks"

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)