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 simulation and distributed inference"
  Abstract: Independent samples from an unknown probability distribution $\mathbf{p}$ on a domain of size $k$ are distributed across $n$ players, with each player holding one sample. Each player can communicate $\ell$ bits to a central referee in a simultaneous message passing (SMP) model of communication to help the referee infer a property of the unknown $\mathbf{p}$. When $\ell\geq\log k$ bits, the problem reduces to the well-studied collocated case where all the samples are available in one place. In this work, we focus on the communication-starved setting of $\ell < \log k$, in which the landscape may change drastically. We propose a general formulation for inference problems in this distributed setting, and instantiate it to two prototypical inference questions: learning and uniformity testing.

Connected through hyperedge HE-1 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "detecting correlations with little memory and communication"
    - "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-2 (induced by source paper "distributed learning with sublinear communication"):
  with members:
    - "introduction to online convex optimization"
    - "space lower bounds for linear prediction"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "communication compression for decentralized training"
    - "detecting correlations with little memory and communication"
    - "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-3 (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"
    - "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-4 (induced by source paper "optimal communication rates and combinatorial properties for distributed simulation"):
  with members:
    - "domain compression and its application to randomness optimal distributed goodness of fit"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "coordination using individually shared randomness"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "resource efficient common randomness and secret key schemes"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "strong coordination over multi hop line networks"

Connected through hyperedge HE-5 (induced by source paper "optimal communication rates for zero error distributed simulation under blackboard communication protocols"):
  with members:
    - "domain compression and its application to randomness optimal distributed goodness of fit"
    - "coordination using individually shared randomness"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "strong coordination over multi hop line networks"
    - "strong coordination over a line network"
    - "common information and secret key capacity"

Connected through hyperedge HE-6 (induced by source paper "lower bounds for learning distributions under communication constraints via fisher information"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "mutual information optimally local private discrete distribution estimation"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "on communication cost of distributed statistical estimation and dimensionality"

Connected through hyperedge HE-7 (induced by source paper "minimax bounds for distributed logistic regression"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "federated learning strategies for improving communication efficiency"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "communication efficient learning of deep networks from decentralized data"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "on communication cost of distributed statistical estimation and dimensionality"

Connected through hyperedge HE-8 (induced by source paper "geometric lower bounds for distributed parameter estimation under communication constraints"):
  with members:
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "distance based and continuum fano inequalities with applications to statistical estimation"
    - "distributed learning communication complexity and privacy"
    - "optimal distributed online prediction using mini batches"

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.DS(Data Structures and Algorithms)