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: "loss minimization and parameter estimation with heavy tails"
  Abstract: This work studies applications and generalizations of a simple estimation technique that provides exponential concentration under heavy-tailed distributions, assuming only bounded low-order moments. We show that the technique can be used for approximate minimization of smooth and strongly convex losses, and specifically for least squares linear regression. For instance, our $d$-dimensional estimator requires just $\tilde{O}(d\log(1/\delta))$ random samples to obtain a constant factor approximation to the optimal least squares loss with probability $1-\delta$, without requiring the covariates or noise to be bounded or subgaussian. We provide further applications to sparse linear regression and low-rank covariance matrix estimation with similar allowances on the noise and covariate distributions. The core technique is a generalization of the median-of-means estimator to arbitrary metric spaces.

Connected through hyperedge HE-1 (induced by source paper "tadam a robust stochastic gradient optimizer"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "a robust adaptive stochastic gradient method for deep learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximal policy optimization algorithms"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"

Connected through hyperedge HE-2 (induced by source paper "understanding the limitations of network online learning"):
  with members:
    - "selective harvesting over networks"
    - "node2vec scalable feature learning for networks"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "network sampling from static to streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-3 (induced by source paper "high dimensional robust m estimation arbitrary corruption and heavy tails"):
  with members:
    - "high dimensional robust sparse regression"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "securing distributed machine learning in high dimensions"
    - "iteratively learning from the best"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "adaptive hard thresholding for near optimal consistent robust regression"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "robust estimators in high dimensions without the computational intractability"
    - "robust regression via hard thresholding"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on iterative hard thresholding methods for high dimensional m estimation"

Connected through hyperedge HE-5 (induced by source paper "kernel truncated randomized ridge regression optimal rates and low noise acceleration"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "understanding deep learning requires rethinking generalization"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "an identity for kernel ridge regression"

Connected through hyperedge HE-6 (induced by source paper "learning with non convex truncated losses by sgd"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "learning without concentration"
    - "from stochastic mixability to fast rates"
    - "relative deviation learning bounds and generalization with unbounded loss functions"

Connected through hyperedge HE-7 (induced by source paper "byzantine robust distributed learning towards optimal statistical rates"):
  with members:
    - "byzantine tolerant machine learning"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-8 (induced by source paper "ell_1 regression with heavy tailed distributions"):
  with members:
    - "learning without concentration"

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)