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: "deformable generator network unsupervised disentanglement of appearance and geometry"
  Abstract: We propose a deformable generator model to disentangle the appearance and geometric information from images into two independent latent vectors. The appearance generator produces the appearance information, including color, illumination, identity or category, of an image. The geometric generator produces displacement of the coordinates of each pixel and performs geometric warping, such as stretching and rotation, on the appearance generator to obtain the final synthesized image. The proposed model can learn both representations from image data in an unsupervised manner. The learned geometric generator can be conveniently transferred to the other image datasets to facilitate downstream AI tasks.

Connected through hyperedge HE-1 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry"):
  with members:
    - "robust facial landmark detection via aggregation on geometrically manipulated faces"
    - "fast geometrically perturbed adversarial faces"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "improving facial attribute prediction using semantic segmentation"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"

Connected through hyperedge HE-2 (induced by source paper "mixnmatch multifactor disentanglement and encoding for conditional image generation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "synthesizing images of humans in unseen poses"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised part based disentangling of object shape and appearance"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "unsupervised discovery of object landmarks as structural representations"
    - "unsupervised disentangled representation learning with analogical relations"
    - "dgpose disentangled semi supervised deep generative models for human body analysis"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "in defense of the triplet loss for person re identification"
    - "pose guided person image generation"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-4 (induced by source paper "explicit disentanglement of appearance and perspective in generative models"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "destnet densely fused spatial transformer networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "towards a definition of disentangled representations"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "improving variational inference with inverse autoregressive flow"

Connected through hyperedge HE-5 (induced by source paper "disentangled deep autoencoding regularization for robust image classification"):
  with members:
    - "unsupervised disentangled representation learning with analogical relations"
    - "learning to decompose and disentangle representations for video prediction"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "decomposing motion and content for natural video sequence prediction"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "unsupervised learning of disentangled representations from video"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "video generation from text"

Connected through hyperedge HE-6 (induced by source paper "learning feature to feature translator by alternating back propagation for zero shot learning"):
  with members:
    - "video to video synthesis"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "generative adversarial text to image synthesis"

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)