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: "kernel exponential family estimation via doubly dual embedding"
  Abstract: We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space. Key to our approach is a novel technique, doubly dual embedding, that avoids computation of the partition function. This technique also allows the development of a flexible sampling strategy that amortizes the cost of Monte-Carlo sampling in the inference stage. The resulting estimator can be easily generalized to kernel conditional exponential families. We furthermore establish a connection between infinite-dimensional exponential family estimation and MMD-GANs, revealing a new perspective for understanding GANs. Compared to current score matching based estimators, the proposed method improves both memory and time efficiency while enjoying stronger statistical properties, such as fully capturing smoothness in its statistical convergence rate while the score matching estimator appears to saturate. Finally, we show that the proposed estimator can empirically outperform state-of-the-art methods in both kernel exponential family estimation and its conditional extension.

Connected through hyperedge HE-1 (induced by source paper "training deep energy based models with f divergence minimization"):
  with members:
    - "exponential family estimation via adversarial dynamics embedding"
    - "maximum entropy generators for energy based models"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "density estimation using real nvp"

Connected through hyperedge HE-2 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "importance weighting and variational inference"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "hamiltonian variational auto encoder"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "stochastic video generation with a learned prior"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "exponential family estimation via adversarial dynamics embedding"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "hamiltonian variational auto encoder"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"
    - "density estimation using real nvp"

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)