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: "distributed functional scalar quantization simplified"
  Abstract: Distributed functional scalar quantization (DFSQ) theory provides optimality conditions and predicts performance of data acquisition systems in which a computation on acquired data is desired. We address two limitations of previous works: prohibitively expensive decoder design and a restriction to source distributions with bounded support. We show that a much simpler decoder has equivalent asymptotic performance to the conditional expectation estimator studied previously, thus reducing decoder design complexity. The simpler decoder features decoupled communication and computation blocks. Moreover, we extend the DFSQ framework with the simpler decoder to source distributions with unbounded support. Finally, through simulation results, we demonstrate that performance at moderate coding rates is well predicted by the asymptotic analysis, and we give new insight on the rate of convergence.

Connected through hyperedge HE-1 (induced by source paper "on distributed quantization for classification"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "training and inference with integers in deep neural networks"
    - "rate distortion for model compression from theory to practice"
    - "neural joint source channel coding"
    - "distributed simulation and distributed inference"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-2 (induced by source paper "compression for multiple reconstructions"):
  with members:
    - "system aware compression"
    - "optimized pre compensating compression"
    - "perceptually optimized image rendering"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

Connected through hyperedge HE-3 (induced by source paper "interactive scalar quantization for distributed resource allocation"):
  with members:
    - "the non regular ceo problem"
    - "overhead performance tradeoffs a resource allocation perspective"
    - "distributed function computation over a rooted directed tree"
    - "multiterminal source coding under logarithmic loss"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "interaction strictly improves the wyner ziv rate distortion function"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-4 (induced by source paper "interactive scalar quantization for distributed extremization"):
  with members:
    - "distributed function computation over a rooted directed tree"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "interaction strictly improves the wyner ziv rate distortion function"

Connected through hyperedge HE-5 (induced by source paper "distributed quantization for compressed sensing"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "analysis by synthesis quantization for compressed sensing measurements"
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "compressive sensing over networks"
    - "universal rate efficient scalar quantization"
    - "distributed compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "intersensor collaboration in distributed quantization networks"):
  with members:
    - "on the dispersions of three network information theory problems"
    - "on cooperation in multi terminal computation and rate distortion"
    - "fixed length lossy compression in the finite blocklength regime"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "the dispersion of lossy source coding"
    - "perfect omniscience perfect secrecy and steiner tree packing"

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)