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: "model agnostic time series analysis via matrix estimation"
  Abstract: We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-noise observed entries, and performing linear regression to make predictions. At the core of our analysis is a representation result, which states that for a large model class, the transformed time series matrix is (approximately) low-rank. In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation. The key to establishing this link is constructing a Page matrix with non-overlapping entries rather than a Hankel matrix as is commonly done in the literature (e.g., SSA). This particular matrix structure allows us to provide finite sample analysis for imputation and prediction, and prove the asymptotic consistency of our method. Another salient feature of our algorithm is that it is model agnostic with respect to both the underlying time dynamics and the noise distribution in the observations. The noise agnostic property of our approach allows us to recover the latent states when only given access to noisy and partial observations a la a Hidden Markov Model; e.g., recovering the time-varying parameter of a Poisson process without knowing that the underlying process is Poisson. Furthermore, since our forecasting algorithm requires regression with noisy features, our approach suggests a matrix estimation based method - coupled with a novel, non-standard matrix estimation error metric - to solve the error-in-variable regression problem, which could be of interest in its own right. Through synthetic and real-world datasets, we demonstrate that our algorithm outperforms standard software packages (including R libraries) in the presence of missing data as well as high levels of noise.

Connected through hyperedge HE-1 (induced by source paper "block hankel tensor arima for multiple short time series forecasting"):
  with members:
    - "manifold modeling in embedded space a perspective for interpreting deep image prior"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "xgboost a scalable tree boosting system"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"

Connected through hyperedge HE-2 (induced by source paper "harnessing structures for value based planning and reinforcement learning"):
  with members:
    - "me net towards effective adversarial robustness with matrix estimation"
    - "deepmind control suite"
    - "implicit quantile networks for distributional reinforcement learning"
    - "count based exploration with neural density models"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "high dimensional stochastic optimal control using continuous tensor decompositions"
    - "deep exploration via bootstrapped dqn"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-3 (induced by source paper "me net towards effective adversarial robustness with matrix estimation"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "adversarially robust generalization requires more data"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-4 (induced by source paper "on robustness of principal component regression"):
  with members:
    - "harnessing structures for value based planning and reinforcement learning"
    - "why are big data matrices approximately low rank"
    - "nice latent variable models have log rank"
    - "bayesian estimation from few samples community detection and related problems"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-5 (induced by source paper "tspdb time series predict db"):
  with members:
    - "on robustness of principal component regression"
    - "bayesdb a probabilistic programming system for querying the probable implications of data"

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)