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: "lossy source coding with reconstruction privacy"
  Abstract: We consider the problem of lossy source coding with side information under a privacy constraint that the reconstruction sequence at a decoder should be kept secret to a certain extent from another terminal such as an eavesdropper, a sender, or a helper. We are interested in how the reconstruction privacy constraint at a particular terminal affects the rate-distortion tradeoff. In this work, we allow the decoder to use a random mapping, and give inner and outer bounds to the rate-distortion-equivocation region for different cases where the side information is available non-causally and causally at the decoder. In the special case where each reconstruction symbol depends only on the source description and current side information symbol, the complete rate-distortion-equivocation region is provided. A binary example illustrating a new tradeoff due to the new privacy constraint, and a gain from the use of a stochastic decoder is given.

Connected through hyperedge HE-1 (induced by source paper "lossy compression with privacy constraints optimality of polar codes"):
  with members:
    - "polar codes for broadcast channels"
    - "asymmetric error correction and flash memory rewriting using polar codes"
    - "achieving marton s region for broadcast channels using polar codes"
    - "from the information bottleneck to the privacy funnel"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "rate distortion theory for secrecy systems"
    - "calibrating data to sensitivity in private data analysis"
    - "polar codes are optimal for lossy source coding"

Connected through hyperedge HE-2 (induced by source paper "secret key based identification and authentication with a privacy constraint"):
  with members:
    - "secret key based authentication with a privacy constraint"
    - "secure multiterminal source coding with side information at the eavesdropper"
    - "secure biometrics concepts authentication architectures and challenges"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"
    - "authentication with distortion criteria"

Connected through hyperedge HE-3 (induced by source paper "secret key based authentication with a privacy constraint"):
  with members:
    - "secure multiterminal source coding with side information at the eavesdropper"
    - "secure biometrics concepts authentication architectures and challenges"
    - "authentication with distortion criteria"

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)