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: "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
  Abstract: ABSTRACTFinding parameters that minimise a loss function is at the core of many machine learning methods. The Stochastic Gradient Descent (SGD) algorithm is widely used and delivers state-of-the-ar...

Connected through hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "how noise affects the hessian spectrum in overparameterized neural networks"):
  with members:
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on connected sublevel sets in deep learning"
    - "gradient descent finds global minima of deep neural networks"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"

Connected through hyperedge HE-3 (induced by source paper "dimensionality compression and expansion in deep neural networks"):
  with members:
    - "geometry of deep generative models for disentangled representations"
    - "the local dimension of deep manifold"
    - "understanding convolutional neural networks with information theory an initial exploration"
    - "fisher rao metric geometry and complexity of neural networks"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "the riemannian geometry of deep generative models"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "parallel stochastic optimization framework for large scale non convex stochastic problems"):
  with members:
    - "an energy efficient controller for wirelessly powered communication networks"
    - "efficient fair and qos aware policies for wirelessly powered communication networks"
    - "accelerating minibatch stochastic gradient descent using typicality sampling"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on breast cancer detection an application of machine learning algorithms on the wisconsin diagnostic dataset"
    - "optimal hierarchical radio resource management for hetnets with flexible backhaul"
    - "adam a method for stochastic optimization"
    - "flexible parallel algorithms for big data optimization"

Connected through hyperedge HE-5 (induced by source paper "training deep neural networks by optimizing over nonlocal paths in hyperparameter space"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "population based training of neural networks"
    - "deep residual learning for image recognition"
    - "the loss surfaces of multilayer networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "efficient convnets for analog arrays"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "imagenet training in minutes"
    - "perforatedcnns acceleration through elimination of redundant convolutions"
    - "convolutional neural networks at constrained time cost"
    - "fractional max pooling"
    - "cudnn efficient primitives for deep learning"

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)