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: "analog network coding in the generalized high snr regime"
  Abstract: In a recent paper [4], Mari\'c et al. analyzed the performance of the analog network coding (ANC) in a layered relay network for the high-SNR regime. They have proved that under the ANC scheme, if each relay transmits the received signals at the upper bound of the power constraint, the transmission rate will approach the network capacity. In this paper, we consider a more general scenario defined as the generalized high-SNR regime, where the relays at layer $l$ in a layered relay network with $L$ layers do not satisfy the high-SNR conditions, and then determine an ANC relay scheme in such network. By relating the received SNR at the nodes with the propagated noise, we derive the rate achievable by the ANC scheme proposed in this paper. The result shows that the achievable ANC rate approaches the upper bound of the ANC capacity as the received powers at relays in high SNR increase. A comparison of the two ANC schemes implies that the scheme proposed in [4] may not always be the optimal one in the generalized high-SNR regime. The result also demonstrates that the upper and lower bounds of the ANC rate coincide in the limit as the number of relays at layer L-1 dissatisfying the high-SNR conditions tends to infinity, yielding an asymptotic capacity result.

Connected through hyperedge HE-1 (induced by source paper "analog network coding in nonlinear chains"):
  with members:
    - "analog network coding in general snr regime"
    - "analog network coding in general snr regime performance of a greedy scheme"
    - "amplify and forward in wireless relay networks"
    - "network beamforming using relays with perfect channel information"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-2 (induced by source paper "analog network coding in general snr regime performance of network simplification"):
  with members:
    - "analog network coding in general snr regime"
    - "analog network coding in general snr regime performance of a greedy scheme"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "amplify and forward in wireless relay networks"
    - "wireless network simplification the gaussian n relay diamond network"
    - "a simple cooperative diversity method based on network path selection"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-3 (induced by source paper "analog network coding in general snr regime performance of a greedy scheme"):
  with members:
    - "analog network coding in general snr regime"
    - "amplify and forward in wireless relay networks"
    - "wireless network simplification the gaussian n relay diamond network"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-4 (induced by source paper "analog network coding in general snr regime"):
  with members:
    - "amplify and forward in wireless relay networks"
    - "optimal relay functionality for snr maximization in memoryless relay 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)