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: "arithmetic distribution matching"
  Abstract: In this work, arithmetic distribution matching (ADM) is presented. ADM invertibly transforms a discrete memoryless source (DMS) into a target DMS. ADM can be used for probabilistic shaping and for rate adaption. Opposed to existing algorithms for distribution matching, ADM works online and can transform arbitrarily long input sequences. It is shown analytically that as the input length tends to infinity, the ADM output perfectly emulates the target DMS with respect to the normalized informational divergence and the entropy rate. Numerical results are presented that confirm the analytical bounds.

Connected through hyperedge HE-1 (induced by source paper "rate constrained shaping codes for structured sources"):
  with members:
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "performance of optimal data shaping codes"
    - "constant composition distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "fixed to variable length distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-2 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems"):
  with members:
    - "binary message passing decoding of product codes based on generalized minimum distance decoding"
    - "binary message passing decoding of product like codes"
    - "iterative bounded distance decoding of product codes with scaled reliability"
    - "on low complexity decoding of product codes for high throughput fiber optic systems"
    - "energy efficient soft assisted product decoders"
    - "increasing achievable information rates via geometric shaping"
    - "achievable rates for probabilistic shaping"
    - "miscorrection free decoding of staircase codes"

Connected through hyperedge HE-3 (induced by source paper "a compressed sensing approach for distribution matching"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"

Connected through hyperedge HE-4 (induced by source paper "probabilistic amplitude shaping with hard decision decoding and staircase codes"):
  with members:
    - "probabilistically shaped coded modulation with hard decision decoding for coherent optical systems"
    - "achievable rates for probabilistic shaping"
    - "high throughput probabilistic shaping with product distribution matching"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "density evolution for deterministic generalized product codes with higher order modulation"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "density evolution for deterministic generalized product codes on the binary erasure channel at high rates"
    - "constant composition distribution matching"

Connected through hyperedge HE-5 (induced by source paper "divergence scaling of fixed length binary output one to one distribution matching"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "constant composition 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-6 (induced by source paper "constant composition distribution matching"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "how to achieve the capacity of asymmetric channels"
    - "block to block distribution matching"
    - "fixed to variable length distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"
    - "operating ldpc codes with zero shaping gap"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-7 (induced by source paper "bandwidth efficient and rate matched low density parity check coded modulation"):
  with members:
    - "protograph based ldpc code design for bit metric decoding"
    - "how to achieve the capacity of asymmetric channels"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "optimal non uniform mapping for probabilistic shaping"
    - "operating ldpc codes with zero shaping gap"
    - "constellation shaping for bit interleaved ldpc coded apsk"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-8 (induced by source paper "ldpc coded modulation with probabilistic shaping for optical fiber systems"):
  with members:
    - "constant composition distribution matching"
    - "probabilistic signal shaping for bit metric decoding"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"
    - "staircase codes fec for 100 gb s otn"

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)