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 codes energy efficient short range communication"
  Abstract: A green code attempts to minimize the total energy per-bit required to communicate across a noisy channel. The classical information-theoretic approach neglects the energy expended in processing the data at the encoder and the decoder and only minimizes the energy required for transmissions. Since there is no cost associated with using more degrees of freedom, the traditionally optimal strategy is to communicate at rate zero. #R##N#In this work, we use our recently proposed model for the power consumed by iterative message passing. Using generalized sphere-packing bounds on the decoding power, we find lower bounds on the total energy consumed in the transmissions and the decoding, allowing for freedom in the choice of the rate. We show that contrary to the classical intuition, the rate for green codes is bounded away from zero for any given error probability. In fact, as the desired bit-error probability goes to zero, the optimizing rate for our bounds converges to 1.

Connected through hyperedge HE-1 (induced by source paper "energy harvesting wireless communications a review of recent advances"):
  with members:
    - "renewable powered cellular networks energy field modeling and network coverage"
    - "dynamic compression transmission for energy harvesting multihop networks with correlated sources"
    - "constrained codes for joint energy and information transfer"
    - "energy harvesting broadband communication systems with processing energy cost"
    - "spatial throughput of mobile ad hoc networks powered by energy harvesting"
    - "energy cooperation in energy harvesting communications"
    - "interactive joint transfer of energy and information"
    - "opportunistic wireless energy harvesting in cognitive radio 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)