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: "gdpp learning diverse generations using determinantal point process"
  Abstract: Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images. An essential characteristic of generative models is their ability to produce multi-modal outputs. However, while training, they are often susceptible to mode collapse, that is models are limited in mapping input noise to only a few modes of the true data distribution. In this work, we draw inspiration from Determinantal Point Process (DPP) to propose an unsupervised penalty loss that alleviates mode collapse while producing higher quality samples. DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity. We use DPP kernel to model the diversity in real data as well as in synthetic data. Then, we devise an objective term that encourages generators to synthesize data with similar diversity to real data. In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme. Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, generation quality, and convergence-time whereas being 5.8x faster than its closest competitor.

Connected through hyperedge HE-1 (induced by source paper "padgan a generative adversarial network for performance augmented diverse designs"):
  with members:
    - "design and evaluation of product aesthetics a human machine hybrid approach"
    - "deep generative design integration of topology optimization and generative models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "beziergan automatic generation of smooth curves from interpretable low dimensional parameters"
    - "mggan solving mode collapse using manifold guided training"
    - "ranking ideas for diversity and quality"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "best of many samples distribution matching"):
  with members:
    - "joint wasserstein autoencoders for aligning multimodal embeddings"
    - "learning implicit generative models with the method of learned moments"
    - "mixture density generative adversarial networks"
    - "cramer wold autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"

Connected through hyperedge HE-3 (induced by source paper "flexible modeling of diversity with strongly log concave distributions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "fair and diverse dpp based data summarization"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "guess free maximization of submodular and linear sums"
    - "unbiased estimates for linear regression via volume sampling"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"
    - "diversity networks neural network compression using determinantal point processes"

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)