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: "green modulation in proactive wireless sensor networks"
  Abstract: Due to unique characteristics of sensor nodes, choosing energy-efficient modulation scheme with low-complexity implementation (refereed to as green modulation) is a critical factor in the physical layer of Wireless Sensor Networks (WSNs). This paper presents (to the best of our knowledge) the first in-depth analysis of energy efficiency of various modulation schemes using realistic parameters in IEEE 802.15.4 standard and present state-of-the art technology, to find the best scheme in a proactive WSN over Rayleigh and Rician flat-fading channel models with path-loss. For this purpose, we describe the proactive system model according to a pre-determined timebased process in practical sensor nodes. The present analysis also includes the effect of channel bandwidth and active mode duration on energy efficiency of popular modulation designs in the pass-band and Ultra-WideBand (UWB) categories. Experimental results show that among various pass-band and UWB modulation schemes, NonCoherent M-ary Frequency Shift Keying (NC-MFSK) with small order of M and On-Off Keying (OOK) have significant energy saving compared to other schemes for short range scenarios, and could be considered as realistic candidates in proactive WSNs. In addition, NC-MFSK and OOK have the advantage of less complexity and cost in implementation than the other schemes.

Connected through hyperedge HE-1 (induced by source paper "on the energy efficiency of lt codes in proactive wireless sensor networks"):
  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:
    - "on the energy efficiency of lt codes 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)