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: "indirect rate distortion function of a binary i i d source"
  Abstract: The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a binary symmetric channel so that the channel crossover probability controls the amount of information available about the source realization at the encoder. We use classic results in rate-distortion theory to compute an expression of the rate-distortion function for this model, where the Bernoulli source is not necessarily symmetric. The indirect rate-distortion function is given in terms of a solution to a simple equation. In addition, we derive an upper bound on the indirect rate-distortion function which is given in a closed. These expressions capture precisely the expected behavior that the noisier the observations, the smaller the return from increasing bit-rate to reduce distortion.

Connected through hyperedge HE-1 (induced by source paper "on zero delay rdf for vector valued gauss markov sources with additional noise"):
  with members:
    - "zero delay rate distortion via filtering for vector valued gaussian sources"
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "directed information on abstract spaces properties and variational equalities"
    - "internet of things iot a vision architectural elements and future directions"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"

Connected through hyperedge HE-2 (induced by source paper "fundamental distortion limits of analog to digital compression"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "sampling and distortion tradeoffs for bandlimited periodic signals"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "lossy computing of correlated sources with fractional sampling"
    - "optimal phase transitions in compressed sensing"
    - "high resolution distributed sampling of bandlimited fields with low precision sensors"

Connected through hyperedge HE-3 (induced by source paper "mismatched multiterminal source coding"):
  with members:
    - "a connection between good rate distortion codes and backward dmcs"

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)