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: "the likelihood encoder for lossy source compression"
  Abstract: In this work, a likelihood encoder is studied in the context of lossy source compression. The analysis of the likelihood encoder is based on a soft-covering lemma. It is demonstrated that the use of a likelihood encoder together with the soft-covering lemma gives alternative achievability proofs for classical source coding problems. The case of the rate-distortion function with side information at the decoder (i.e. the Wyner-Ziv problem) is carefully examined and an application of the likelihood encoder to the multi-terminal source coding inner bound (i.e. the Berger-Tung region) is outlined.

Connected through hyperedge HE-1 (induced by source paper "the likelihood encoder for lossy compression"):
  with members:
    - "joint source channel secrecy using hybrid coding"
    - "a rate distortion based secrecy system with side information at the decoders"
    - "a generalized typicality for abstract alphabets"
    - "a technique for deriving one shot achievability results in network information theory"
    - "non asymptotic output statistics of random binning and its applications"
    - "a connection between good rate distortion codes and backward dmcs"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"

Connected through hyperedge HE-2 (induced by source paper "secure cascade channel synthesis"):
  with members:
    - "strong coordination over multi hop line networks"
    - "secrecy in cascade networks"
    - "strong coordination over a line network"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "achievability proof via output statistics of random binning"
    - "secure channel simulation"
    - "channel simulation via interactive communications"

Connected through hyperedge HE-3 (induced by source paper "semantic security capacity for wiretap channels of type ii"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "the likelihood encoder for lossy compression"
    - "soft covering with high probability"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "a rate distortion based secrecy system with side information at the decoders"
    - "semantically secure coding scheme achieving the capacity of a gaussian wiretap channel"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "informational divergence approximations to product distributions"

Connected through hyperedge HE-4 (induced by source paper "joint source channel secrecy using hybrid coding"):
  with members:
    - "rate distortion based physical layer secrecy with applications to multimode fiber"
    - "a rate distortion based secrecy system with side information at the decoders"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "hybrid coding an interface for joint source channel coding and network communication"
    - "source channel secrecy with causal disclosure"
    - "secrecy is cheap if the adversary must reconstruct"

Connected through hyperedge HE-5 (induced by source paper "secret key generation with one communicator and a one shot converse via hypercontractivity"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "secret key agreement general capacity and second order asymptotics"
    - "assisted common information with an application to secure two party sampling"
    - "distributed channel synthesis"

Connected through hyperedge HE-6 (induced by source paper "resolvability in e γ with applications to lossy compression and wiretap channels"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "distributed channel synthesis"
    - "reliability and secrecy functions of the wiretap channel under cost constraint"
    - "strong secrecy from channel resolvability"

Connected through hyperedge HE-7 (induced by source paper "a rate distortion based secrecy system with side information at the decoders"):
  with members:
    - "secure transmission of sources over noisy channels with side information at the receivers"
    - "a bit of secrecy for gaussian source compression"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "secrecy is cheap if the adversary must reconstruct"
    - "using a secret key to foil an eavesdropper"
    - "secure lossy source coding with side information at the decoders"

Connected through hyperedge HE-8 (induced by source paper "the henchman problem measuring secrecy by the minimum distortion in a list"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "on the shannon cipher system with a wiretapper guessing subject to distortion and reliability requirements"

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)