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: "on the shannon cipher system with a capacity limited key distribution channel"
  Abstract: We consider the Shannon cipher system in a setting where the secret key is delivered to the legitimate receiver via a channel with limited capacity. For this setting, we characterize the achievable region in the space of three figures of merit: the security (measured in terms of the equivocation), the compressibility of the cryptogram, and the distortion associated with the reconstruction of the plaintext source. Although lossy reconstruction of the plaintext does not rule out the option that the (noisy) decryption key would differ, to a certain extent, from the encryption key, we show, nevertheless, that the best strategy is to strive for perfect match between the two keys, by applying reliable channel coding to the key bits, and to control the distortion solely via rate-distortion coding of the plaintext source before the encryption. In this sense, our result has a flavor similar to that of the classical source-channel separation theorem. Some variations and extensions of this model are discussed as well.

Connected through hyperedge HE-1 (induced by source paper "a large deviations approach to secure lossy compression"):
  with members:
    - "measuring secrecy by the probability of a successful guess"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"
    - "rate distortion theory for secrecy systems"
    - "errors and erasures decoding for block codes with feedback"
    - "secrecy is cheap if the adversary must reconstruct"
    - "using a secret key to foil an eavesdropper"
    - "secure lossless compression with side information"

Connected through hyperedge HE-2 (induced by source paper "lossy source coding with reconstruction privacy"):
  with members:
    - "secure multiterminal source coding with side information at the eavesdropper"
    - "rate distortion theory for secrecy systems"
    - "discriminatory lossy source coding side information privacy"
    - "secure source coding with a helper"
    - "lossless compression with security constraints"
    - "on joint coding for watermarking and encryption"

Connected through hyperedge HE-3 (induced by source paper "secure multiterminal source coding with side information at the eavesdropper"):
  with members:
    - "rate distortion with side information at many decoders"
    - "information theoretic proofs of entropy power inequalities"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "rate region of the gaussian scalar help vector source coding problem"
    - "secure lossy source coding with side information at the decoders"
    - "construction of wiretap codes from ordinary channel codes"
    - "lecture notes on network information theory"
    - "secure source coding with a helper"

Connected through hyperedge HE-4 (induced by source paper "secure lossy transmission of vector gaussian sources"):
  with members:
    - "rate region of the gaussian scalar help vector source coding problem"
    - "secure lossy source coding with side information at the decoders"
    - "a framework for partial secrecy"
    - "secure source coding with a helper"
    - "the secrecy capacity region of the gaussian mimo multi receiver wiretap channel"
    - "secure lossless compression with side information"
    - "lossless compression with security constraints"
    - "an extremal inequality motivated by multiterminal information theoretic problems"

Connected through hyperedge HE-5 (induced by source paper "secure lossy source channel wiretapping with side information at the receiving terminals"):
  with members:
    - "secure lossy source coding with side information at the decoders"
    - "secure source coding with a helper"
    - "capacity regions of two new classes of 2 receiver broadcast channels"
    - "secure lossless compression with side information"
    - "lossless compression with security constraints"
    - "shannon s secrecy system with informed receivers and its application to systematic coding for wiretapped channels"

Connected through hyperedge HE-6 (induced by source paper "secure lossy source coding with side information at the decoders"):
  with members:
    - "lecture notes on network information theory"
    - "secure source coding with a helper"
    - "secure lossless compression with side information"
    - "lossless compression with security constraints"
    - "shannon s secrecy system with informed receivers and its application to systematic coding for wiretapped 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)