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: "secrecy capacity of the gaussian wire tap channel with finite complex constellation input"
  Abstract: The secrecy capacity of a discrete memoryless Gaussian Wire-Tap Channel when the input is from a finite complex constellation is studied. It is shown that the secrecy capacity curves of a finite constellation plotted against the SNR, for a fixed noise variance of the eavesdropper's channel has a global maximum at an internal point. This is in contrast to what is known in the case of Gaussian codebook input where the secrecy capacity curve is a bounded, monotonically increasing function of SNR. Secrecy capacity curves for some well known constellations like BPSK, 4-QAM, 16-QAM and 8-PSK are plotted and the SNR at which the maximum occurs is found through simulation. It is conjectured that the secrecy capacity curves for finite constellations have a single maximum.

Connected through hyperedge HE-1 (induced by source paper "mimo df relay beamforming for secrecy with artificial noise imperfect csi and finite alphabet"):
  with members:
    - "power allocation in mimo wiretap channel with statistical csi and finite alphabet input"
    - "robust secure transmission in miso channels based on worst case optimization"
    - "on secrecy rate analysis of mimo wiretap channels driven by finite alphabet input"
    - "on cooperative relaying schemes for wireless physical layer security"
    - "collaborative relay beamforming for secrecy"
    - "the secrecy capacity of the mimo wiretap channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "power allocation in mimo wiretap channel with statistical csi and finite alphabet input"):
  with members:
    - "full rank solutions for the mimo gaussian wiretap channel with an average power constraint"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "on ergodic secrecy capacity for gaussian miso wiretap channels"
    - "secure transmission with multiple antennas ii the mimome wiretap channel"
    - "the secrecy capacity of the mimo wiretap channel"

Connected through hyperedge HE-3 (induced by source paper "secrecy transmission on block fading channels theoretical limits and performance of practical codes"):
  with members:
    - "on secrecy capacity of fast fading multiple input wiretap channels with statistical csit"
    - "a physical layer secured key distribution technique for ieee 802 11g wireless networks"
    - "coding with scrambling concatenation and harq for the awgn wire tap channel a security gap analysis"
    - "on secrecy rate of the generalized artificial noise assisted secure beamforming for wiretap channels"
    - "secret sharing ldpc codes for the bpsk constrained gaussian wiretap channel"
    - "increasing physical layer security through scrambled codes and arq"
    - "rethinking the secrecy outage formulation a secure transmission design perspective"
    - "achieving the secrecy capacity of wiretap channels using polar codes"

Connected through hyperedge HE-4 (induced by source paper "power allocation and time domain artificial noise design for wiretap ofdm with discrete inputs"):
  with members:
    - "on secrecy rate analysis of mimo wiretap channels driven by finite alphabet input"
    - "mimo multiple access channel with an arbitrarily varying eavesdropper"
    - "optimal and robust transmit designs for miso channel secrecy by semidefinite programming"
    - "solutions for the mimo gaussian wiretap channel with a cooperative jammer"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "secure transmission with multiple antennas ii the mimome wiretap channel"
    - "the wiretap channel with feedback encryption over the channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "a novel power allocation scheme for two user gmac with finite input constellations"):
  with members:
    - "linear precoding for relay networks with finite alphabet constraints"
    - "bounds on the achievable rate for the fading relay channel with finite input constellations"
    - "achievable rates in two user interference channels with finite inputs and very strong interference"

Connected through hyperedge HE-6 (induced by source paper "bounds on the achievable rate for the fading relay channel with finite input constellations"):
  with members:
    - "two user gaussian interference channel with finite constellation input and fdma"

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)