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: "inception score label smoothing gradient vanishing and log d x alternative"
  Abstract: In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. #R##N#--- #R##N#An advanced version is included in arXiv:1703.02000 "Activation Maximization Generative Adversarial Nets". #R##N#Please refer Section 6 in 1703.02000 for detailed analysis on Inception Score, and refer its appendix for the discussions on Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative.

Connected through hyperedge HE-1 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "part guided attention learning for vehicle re identification"
    - "attributes guided feature learning for vehicle re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "the state of sparsity in deep neural networks"
    - "group normalization"
    - "ram a region aware deep model for vehicle re identification"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-2 (induced by source paper "pros and cons of gan evaluation measures"):
  with members:
    - "decoupled learning for conditional adversarial networks"
    - "cross view image synthesis using conditional gans"
    - "generating adversarial examples with adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"

Connected through hyperedge HE-3 (induced by source paper "design design inspiration from generative networks"):
  with members:
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep learning techniques for music generation a survey"
    - "fashioning with networks neural style transfer to design clothes"
    - "a neural representation of sketch drawings"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "closing the ai knowledge gap"):
  with members:
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "parlai a dialog research software platform"
    - "the social dilemma of autonomous vehicles"
    - "the rise of social bots"
    - "openml networked science in machine learning"
    - "imagenet large scale visual recognition challenge"
    - "discrimination in online ad delivery"
    - "the online laboratory conducting experiments in a real labor market"

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)