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: "geometric lower bounds for distributed parameter estimation under communication constraints"
  Abstract: We consider parameter estimation in distributed networks, where each sensor in the network observes an independent sample from an underlying distribution and has $k$ bits to communicate its sample to a centralized processor which computes an estimate of a desired parameter. We develop lower bounds for the minimax risk of estimating the underlying parameter under squared $\ell_2$ loss for a large class of distributions. Our results show that under mild regularity conditions, the communication constraint reduces the effective sample size by a factor of $d$ when $k$ is small, where $d$ is the dimension of the estimated parameter. Furthermore, this penalty reduces at most exponentially with increasing $k$, which is the case for some models, e.g., estimating high-dimensional distributions. For other models however, we show that the sample size reduction is re-mediated only linearly with increasing $k$, e.g. when some sub-Gaussian structure is available. We apply our results to the distributed setting with product Bernoulli model, multinomial model, and dense/sparse Gaussian location models which recover or strengthen existing results. #R##N#Our approach significantly deviates from existing approaches for developing information-theoretic lower bounds for communication-efficient estimation. We circumvent the need for strong data processing inequalities used in prior work and develop a geometric approach which builds on a new representation of the communication constraint. This approach allows us to strengthen and generalize existing results with simpler and more transparent proofs.

Connected through hyperedge HE-1 (induced by source paper "communication and memory efficient testing of discrete distributions"):
  with members:
    - "two party distribution testing communication and security"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "sharp bounds for generalized uniformity testing"
    - "generalized uniformity testing"
    - "testing conditional independence of discrete distributions"
    - "a new approach for testing properties of discrete distributions"

Connected through hyperedge HE-2 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters"):
  with members:
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "linear queries estimation with local differential privacy"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-3 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "vqsgd vector quantized stochastic gradient descent"
    - "distributed learning with sublinear communication"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-4 (induced by source paper "distributed learning with sublinear communication"):
  with members:
    - "introduction to online convex optimization"
    - "space lower bounds for linear prediction"
    - "communication compression for decentralized training"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-5 (induced by source paper "accuracy memory tradeoffs and phase transitions in belief propagation"):
  with members:
    - "the circuit complexity of inference"
    - "communication complexity of estimating correlations"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "extractor based time space lower bounds for learning"
    - "secret key generation with limited interaction"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretic lower bounds for distributed function computation"

Connected through hyperedge HE-6 (induced by source paper "inference under information constraints ii communication constraints and shared randomness"):
  with members:
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "sample optimal identity testing with high probability"
    - "a new approach for testing properties of discrete distributions"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "decidability of non interactive simulation of joint distributions"

Connected through hyperedge HE-7 (induced by source paper "on distributed quantization for classification"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "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"
    - "neural discrete representation learning"

Connected through hyperedge HE-8 (induced by source paper "finite precision stochastic optimization accounting for the bias"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "federated learning strategies for improving communication efficiency"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

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.DC(Distributed, Parallel, and Cluster Computing)