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: "on the energy efficiency of lt codes in proactive wireless sensor networks"
  Abstract: This paper presents an in-depth analysis on the energy efficiency of Luby transform (LT) codes with frequency shift keying (FSK) modulation in a wireless sensor network (WSN) over Rayleigh fading channels with path-loss. We describe a proactive system model according to a flexible duty-cycling mechanism utilized in practical sensor apparatus. The present analysis is based on realistic parameters including the effect of channel bandwidth used in the IEEE 802.15.4 standard, active mode duration, and computation energy. A comprehensive analysis, supported by some simulation studies on the probability mass function of the LT code rate and coding gain, shows that among uncoded FSK and various classical channel coding schemes, the optimized LT coded FSK is the most energy-efficient scheme for distance d greater than the predetermined threshold level dT, where the optimization is performed over coding and modulation parameters. In addition, although the optimized uncoded FSK outperforms coded schemes for d <; dT , the energy gap between LT coded and uncoded FSK is negligible for d <; dT compared to the other coded schemes. These results come from the flexibility of the LT code to adjust its rate to suit instantaneous channel conditions and suggest that LT codes are beneficial in practical low-power WSNs with dynamic position sensor nodes.

Connected through hyperedge HE-1 (induced by source paper "energy efficiency and reliability in wireless biomedical implant systems"):
  with members:
    - "green modulations in energy constrained wireless sensor networks"

Connected through hyperedge HE-2 (induced by source paper "green modulations in energy constrained wireless sensor networks"):
  with members:
    - "green modulation in proactive wireless sensor 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.IT(Information Theory)