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: "square hellinger subadditivity for bayesian networks and its applications to identity testing"
  Abstract: We show that the square Hellinger distance between two Bayesian networks on the same directed graph, $G$, is subadditive with respect to the neighborhoods of $G$. Namely, if $P$ and $Q$ are the probability distributions defined by two Bayesian networks on the same DAG, our inequality states that the square Hellinger distance, $H^2(P,Q)$, between $P$ and $Q$ is upper bounded by the sum, $\sum_v H^2(P_{\{v\} \cup \Pi_v}, Q_{\{v\} \cup \Pi_v})$, of the square Hellinger distances between the marginals of $P$ and $Q$ on every node $v$ and its parents $\Pi_v$ in the DAG. Importantly, our bound does not involve the conditionals but the marginals of $P$ and $Q$. We derive a similar inequality for more general Markov Random Fields. #R##N#As an application of our inequality, we show that distinguishing whether two Bayesian networks $P$ and $Q$ on the same (but potentially unknown) DAG satisfy $P=Q$ vs $d_{\rm TV}(P,Q)>\epsilon$ can be performed from $\tilde{O}(|\Sigma|^{3/4(d+1)} \cdot n/\epsilon^2)$ samples, where $d$ is the maximum in-degree of the DAG and $\Sigma$ the domain of each variable of the Bayesian networks. If $P$ and $Q$ are defined on potentially different and potentially unknown trees, the sample complexity becomes $\tilde{O}(|\Sigma|^{4.5} n/\epsilon^2)$, whose dependence on $n, \epsilon$ is optimal up to logarithmic factors. Lastly, if $P$ and $Q$ are product distributions over $\{0,1\}^n$ and $Q$ is known, the sample complexity becomes $O(\sqrt{n}/\epsilon^2)$, which is optimal up to constant factors.

Connected through hyperedge HE-1 (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans"):
  with members:
    - "naomi non autoregressive multiresolution sequence imputation"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "adversarial audio synthesis"
    - "improved training of wasserstein gans"
    - "on the discrimination generalization tradeoff in gans"
    - "f divergence inequalities"

Connected through hyperedge HE-2 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning"):
  with members:
    - "efficiently learning and sampling interventional distributions from observations"
    - "learning and testing causal models with interventions"
    - "learning graphical models using multiplicative weights"
    - "testing ising models"
    - "testing bayesian networks"
    - "causal networks semantics and expressiveness"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing probability distributions using conditional samples"

Connected through hyperedge HE-3 (induced by source paper "private identity testing for high dimensional distributions"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "improved differentially private analysis of variance"
    - "individual sensitivity preprocessing for data privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "learning and testing causal models with interventions"
    - "privately learning high dimensional distributions"
    - "test without trust optimal locally private distribution testing"

Connected through hyperedge HE-4 (induced by source paper "communication and memory efficient testing of discrete distributions"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "locality sensitive hashing for f divergences mutual information loss and beyond"):
  with members:
    - "categorical feature compression via submodular optimization"
    - "norm ranging lsh for maximum inner product search"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "greedy merge degrading has optimal power law"
    - "f divergence inequalities"
    - "a survey on learning to hash"
    - "hashing for similarity search a survey"
    - "a directed isoperimetric inequality with application to bregman near neighbor lower bounds"

Connected through hyperedge HE-6 (induced by source paper "testing identity of multidimensional histograms"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "sharp bounds for generalized uniformity testing"
    - "fourier based testing for families of distributions"
    - "generalized uniformity testing"
    - "testing conditional independence of discrete distributions"
    - "a new approach for testing properties of discrete distributions"

Connected through hyperedge HE-7 (induced by source paper "learning and testing causal models with interventions"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "testing bayesian networks"
    - "optimal testing for properties of distributions"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal networks semantics and expressiveness"
    - "inequality constraints in causal models with hidden variables"

Connected through hyperedge HE-8 (induced by source paper "sharp bounds for generalized uniformity testing"):
  with members:
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "fourier based testing for families of distributions"
    - "generalized uniformity testing"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"

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.LG(Machine Learning)