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 error probability of stochastic decision and stochastic decoding"
  Abstract: This paper investigates the error probability of a stochastic decision and the way in which it differs from the error probability of an optimal decision, i.e., the maximum a posteriori decision. It is shown that the error probability of a stochastic decision with the a posteriori distribution is at most twice the error probability of the maximum a posteriori decision. Furthermore, it is shown that, by generating an independent identically distributed random sequence subject to the a posteriori distribution and making a decision that maximizes the a posteriori probability over the sequence, the error probability approaches exponentially the error probability of the maximum a posteriori decision as the sequence length increases. Using these ideas as a basis, we can construct stochastic decoders for source/channel codes.

Connected through hyperedge HE-1 (induced by source paper "successive cancellation decoding of linear source code"):
  with members:
    - "channel coding and lossy source coding using a generator of constrained random numbers"

Connected through hyperedge HE-2 (induced by source paper "multi terminal codes using constrained random number generators"):
  with members:
    - "construction of a channel code from an arbitrary source code with decoder side information"
    - "channel coding and lossy source coding using a generator of constrained random numbers"
    - "construction of slepian wolf source code and broadcast channel code based on hash property"

Connected through hyperedge HE-3 (induced by source paper "construction of a channel code from an arbitrary source code with decoder side information"):
  with members:
    - "channel coding and lossy source coding using a generator of constrained random numbers"
    - "achievability proof via output statistics of random binning"
    - "construction of slepian wolf source code and broadcast channel code based on hash property"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "performance of polar codes for channel and source coding"

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)