human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compressed sensing in multi hop large scale wireless sensor networks based on routing topology tomography"):
  Abstract: Data acquisition from a multi-hop large-scale outdoor wireless sensor network (WSN) deployment for environmental monitoring is full of challenges. This is because the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the big data acquisition volume from the large-scale WSN,and the highly dynamic wireless link conditions in an outdoor communication environment. We present a novel compressed sensing approach which can recover the sensing data at the sink with high fidelity when very few data packets are collected, leading to a significant reduction of the net-work transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without a mote's storing of any part of the random projection matrix, as opposed to other existing compressed sensing based schemes. We propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in the compressed sensing for data acquisition. We validate our approach and evaluate its performance using a real-world multi-hop WSN testbed deployment in situ. The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field, while drastically reducing wireless communication costs at the same time.

Includes members:
  Member "link delay estimation via expander graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "link delay estimation using sparse recovery for dynamic network tomography")
  Member "discovering the structure of complex networks by minimizing cyclic bandwidth sum":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "duality between temporal networks and signals extraction of the temporal network structures")
  Member "heuristic for solving cyclic bandwidth sum problem by following the structure of the graph":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the fly approximation of multivariate total variation minimization")
  Member "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")

Question: Please tell me which class the source paper 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)