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: "divergence scaling of fixed length binary output one to one distribution matching"
  Abstract: Distribution matching is the process of mapping a uniformly distributed input sequence onto sequences that approximate the output of a desired discrete memoryless source and the original input sequence can be recovered. The special case of a binary output alphabet and one-to-one mapping is studied. A fixed-length distribution matcher is proposed that is optimal in the sense of minimizing the unnormalized divergence between its output distribution and a binary memoryless target distribution. Upper and lower bounds on the unnormalized divergence are computed that increase logarithmically in the output block length $n$. It follows that a recently proposed constant composition distribution matcher performs within a constant gap of the minimal achievable informational divergence.

Connected through hyperedge HE-1 (induced by source paper "shaped on off keying using polar codes"):
  with members:
    - "probabilistic parity shaping for linear codes"
    - "protograph based ldpc code design for probabilistic shaping with on off keying"
    - "polar coded pulse position modulation for the poisson channel"
    - "code design for short blocks a survey"
    - "constant composition distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "list decoding of polar codes"
    - "how to construct polar codes"

Connected through hyperedge HE-2 (induced by source paper "arithmetic coding based multi composition codes for bit level distribution matching"):
  with members:
    - "shell mapping for distribution matching"
    - "constant composition distribution matching"
    - "block to block distribution matching"

Connected through hyperedge HE-3 (induced by source paper "prefix free code distribution matching for probabilistic constellation shaping"):
  with members:
    - "shell mapping for distribution matching"
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "fixed to variable length resolution coding for target distributions"
    - "constellation shaping for bit interleaved ldpc coded apsk"
    - "matching dyadic distributions to channels"
    - "prefix codes equiprobable words unequal letter costs"

Connected through hyperedge HE-4 (induced by source paper "asymptotic coupling and its applications in information theory"):
  with members:
    - "measuring secrecy by the probability of a successful guess"
    - "fundamental limits of communication with low probability of detection"
    - "distributed simulation of continuous random variables"
    - "covert communication over noisy channels a resolvability perspective"
    - "generalized common information common information extraction and private sources synthesis"
    - "source channel secrecy for shannon cipher system"
    - "on the entropy of couplings"
    - "quantum secure covert communication on bosonic channels"

Connected through hyperedge HE-5 (induced by source paper "high throughput probabilistic shaping with product distribution matching"):
  with members:
    - "probabilistic shaping and non binary codes"
    - "matching dyadic distributions to channels"

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)