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: "optimal quantization for distribution synthesis"
  Abstract: Finite precision approximations of discrete probability distributions are considered, applicable for distribution synthesis, e.g., probabilistic shaping. Two algorithms are presented that find the optimal    $M$   -type approximation    $Q$    of a distribution    $P$    in terms of the variational distance    $\lVert Q-P\rVert _{1}$    and the informational divergence    $D(Q\Vert P)$   . Bounds on the approximation errors are derived and shown to be asymptotically tight. Several examples illustrate that the variational distance optimal approximation can be quite different from the informational divergence optimal approximation.

Connected through hyperedge HE-1 (induced by source paper "a note on reverse pinsker inequalities"):
  with members:
    - "f divergence inequalities"
    - "on reverse pinsker inequalities"

Connected through hyperedge HE-2 (induced by source paper "information rates and error exponents for probabilistic amplitude shaping"):
  with members:
    - "achievable rates for probabilistic shaping"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constant composition distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"

Connected through hyperedge HE-3 (induced by source paper "a mismatched joint source channel coding perspective of probabilistic amplitude shaping achievable rates and error exponents"):
  with members:
    - "achievable rates for probabilistic shaping"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constant composition distribution matching"

Connected through hyperedge HE-4 (induced by source paper "divergence scaling of fixed length binary output one to one distribution matching"):
  with members:
    - "constant composition distribution matching"
    - "arithmetic distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "fixed to variable length distribution matching"
    - "effective secrecy reliability confusion and stealth"
    - "operating ldpc codes with zero shaping gap"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-5 (induced by source paper "finite model approximations for partially observed markov decision processes with discounted cost"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "discretized approximations for pomdp with average cost"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-6 (induced by source paper "f divergence inequalities"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "e_ gamma resolvability"
    - "bounds between contraction coefficients"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "refinement of some moment inequalities"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"

Connected through hyperedge HE-7 (induced by source paper "greedy algorithms for optimal distribution approximation"):
  with members:
    - "constant composition distribution matching"
    - "optimal non uniform mapping for probabilistic shaping"

Connected through hyperedge HE-8 (induced by source paper "bounds among f divergences"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "f divergence inequalities"
    - "on the r e nyi divergence and the joint range of relative entropies"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"
    - "concentration of measure inequalities and their communication and information theoretic applications"

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)