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: "on iterative hard thresholding methods for high dimensional m estimation"
  Abstract: The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard $L_0$ constraints. Of the known methods, the class of projected gradient descent (also known as iterative hard thresholding (IHT)) methods is known to offer the fastest and most scalable solutions. However, the current state-of-the-art is only able to analyze these methods in extremely restrictive settings which do not hold in high dimensional statistical models. In this work we bridge this gap by providing the first analysis for IHT-style methods in the high dimensional statistical setting. Our bounds are tight and match known minimax lower bounds. Our results rely on a general analysis framework that enables us to analyze several popular hard thresholding style algorithms (such as HTP, CoSaMP, SP) in the high dimensional regression setting. We also extend our analysis to a large family of "fully corrective methods" that includes two-stage and partial hard-thresholding algorithms. We show that our results hold for the problem of sparse regression, as well as low-rank matrix recovery.

Connected through hyperedge HE-1 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "discovering neural wirings"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "butterfly transform an efficient fft based neural architecture design"
    - "green ai"

Connected through hyperedge HE-2 (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-3 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "how to escape saddle points efficiently"
    - "a survey on social media anomaly detection"
    - "unregularized online learning algorithms with general loss functions"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "interaction hard thresholding consistent sparse quadratic regression in sub quadratic time and space"):
  with members:
    - "high dimensional robust sparse regression"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "a greedy approach for budgeted maximum inner product search"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "sparse polynomial learning and graph sketching"
    - "compressed matrix multiplication"

Connected through hyperedge HE-5 (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"
    - "loss minimization and parameter estimation with heavy tails"

Connected through hyperedge HE-6 (induced by source paper "best first search algorithm for non convex sparse minimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "anytime heuristic search"

Connected through hyperedge HE-7 (induced by source paper "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"):
  with members:
    - "beyond submodular maximization"
    - "fast parallel algorithms for feature selection"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "greed is still good maximizing monotone submodular supermodular functions"
    - "fast greedy algorithms for dictionary selection with generalized sparsity constraints"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-8 (induced by source paper "fast robust and non convex subspace recovery"):
  with members:
    - "a well tempered landscape for non convex robust subspace recovery"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"
    - "input sparsity and hardness for robust subspace approximation"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "complete dictionary recovery over the sphere"
    - "robust computation of linear models by convex relaxation"
    - "understanding alternating minimization for matrix completion"
    - "algorithms and hardness for robust subspace recovery"

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)