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: "towards approaching total power capacity transmit and decoding power minimization for ldpc codes"
  Abstract: Motivated by recently derived fundamental limits on total (transmit + decoding) power for coded communication, this paper investigates how close regular LDPC codes can get to these fundamental limits. For two decoding algorithms (Gallager-A and Gallager-B), we provide upper and lower bounds on the required decoding power based on models of parallelized decoding implementations. As the target error-probability is lowered to zero, we show that the transmit power must increase unboundedly in order to minimize total (transmit + decoding) power. Complementing our theoretical results, we develop detailed physical models of decoding implementations using rigorous (post-layout) circuit simulations, and use them to provide a framework to search for codes that may minimize total power. Our results show that approaching the total-power channel capacity requires increasing the complexity of both the code design and the corresponding decoding algorithm as the communication distance is increased, or as the target error-probability is lowered.

Connected through hyperedge HE-1 (induced by source paper "on scaling rules for energy of vlsi polar encoders and decoders"):
  with members:
    - "information friction and its implications on minimum energy required for communication"
    - "finite length scaling for polar codes"
    - "energy consumption of vlsi decoders"
    - "a high throughput energy efficient implementation of successive cancellation decoder for polar codes using combinational logic"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "improved bounds on the finite length scaling of polar codes"
    - "hardware architectures for successive cancellation decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

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)