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 "hybrid deec towards efficient energy utilization in wireless sensor networks"):
  Abstract: 4 Abstract: The clustering algorithm are considered as a kind of key technique used to reduce energy consumption. It can help in increasing the stability period and network life time. Routing protocol for efficient energy utilization should be designed for heterogeneous Wireless Sensor Networks (WSNs). We purpose Hybrid-DEEC (H-DEEC), a chain and cluster based (hybrid) distributed scheme for efficient energy utilization in WSNs. In H-DEEC, elected Cluster Heads (CHs) communicate the Base Station (BS) through beta elected nodes, by using multi-hopping. We logically divide the network into two parts, on the basis of the residual energy of nodes. The normal nodes with high initial and residual energy will be highly probable to be CHs than the nodes with lesser energy. To overcome the deficiencies of H-DEEC, we propose Multi-Edged Hybrid-DEEC (MH-DEEC). In MH-DEEC the criteria of chain construction is modified. Finally, the comparison in simulation results with other heterogeneous protocols show that, MH-DEEC and H-DEEC achieves longer stability time and network life time due to efficient energy utilization.

Includes members:
  Member "ceec centralized energy efficient clustering a new routing protocol for wsns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling and evaluating performance of routing operations in reactive routing protocols")
  Member "on performance evaluation of variants of deec in wsns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks")

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)