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 scalar quantization for computing high resolution analysis and extensions"
  Abstract: Communication of quantized information is frequently followed by a computation. We consider situations of distributed functional scalar quantization: distributed scalar quantization of (possibly correlated) sources followed by centralized computation of a function. Under smoothness conditions on the sources and function, companding scalar quantizer designs are developed to minimize mean-squared error (MSE) of the computed function as the quantizer resolution is allowed to grow. Striking improvements over quantizers designed without consideration of the function are possible and are larger in the entropy-constrained setting than in the fixed-rate setting. As extensions to the basic analysis, we characterize a large class of functions for which regular quantization suffices, consider certain functions for which asymptotic optimality is achieved without arbitrarily fine quantization, and allow limited collaboration between source encoders. In the entropy-constrained setting, a single bit per sample communicated between encoders can have an arbitrarily large effect on functional distortion. In contrast, such communication has very little effect in the fixed-rate setting.

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 functional scalar quantization simplified"

Connected through hyperedge HE-3 (induced by source paper "on the interactive communication cost of the distributed nearest lattice point problem"):
  with members:
    - "communication cost of transforming a nearest plane partition to the voronoi partition"
    - "distributed lossy averaging"

Connected through hyperedge HE-4 (induced by source paper "towards a converse for the nearest lattice point problem"):
  with members:
    - "communication cost of transforming a nearest plane partition to the voronoi partition"
    - "on the communication cost of determining an approximate nearest lattice point"

Connected through hyperedge HE-5 (induced by source paper "communication cost of transforming a nearest plane partition to the voronoi partition"):
  with members:
    - "on the communication cost of determining an approximate nearest lattice point"

Connected through hyperedge HE-6 (induced by source paper "optimizing quantization for lasso recovery"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "methods for quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "quantization and compressive sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "message passing de quantization with applications to compressed sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "frame permutation quantization"

Connected through hyperedge HE-7 (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 functional scalar quantization simplified"
    - "distributed function computation over a rooted directed tree"
    - "multiterminal source coding under logarithmic loss"
    - "interaction strictly improves the wyner ziv rate distortion function"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-8 (induced by source paper "representation and coding of signal geometry"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "binary embedding fundamental limits and fast algorithm"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"

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)