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: "beziergan automatic generation of smooth curves from interpretable low dimensional parameters"
  Abstract: Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational B\'ezier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration.

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"
    - "mggan solving mode collapse using manifold guided training"
    - "gdpp learning diverse generations using determinantal point process"
    - "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 "3d shape synthesis for conceptual design and optimization using variational autoencoders"):
  with members:
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "neural networks for topology optimization"
    - "learning a hierarchical latent variable model of 3d shapes"
    - "matterport3d learning from rgb d data in indoor environments"
    - "orientation boosted voxel nets for 3d object recognition"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "fpnn field probing neural networks for 3d data"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "the ind dataset a drone dataset of naturalistic road user trajectories at german intersections"):
  with members:
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "intentions of vulnerable road users detection and forecasting by means of machine learning"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
    - "ssd single shot multibox detector"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"

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)