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: "transformation properties of learned visual representations"
  Abstract: When a three-dimensional object moves relative to an observer, a change occurs on the observer's image plane and in the visual representation computed by a learned model. Starting with the idea that a good visual representation is one that transforms linearly under scene motions, we show, using the theory of group representations, that any such representation is equivalent to a combination of the elementary irreducible representations. We derive a striking relationship between irreducibility and the statistical dependency structure of the representation, by showing that under restricted conditions, irreducible representations are decorrelated. Under partial observability, as induced by the perspective projection of a scene onto the image plane, the motion group does not have a linear action on the space of images, so that it becomes necessary to perform inference over a latent representation that does transform linearly. This idea is demonstrated in a model of rotating NORB objects that employs a latent representation of the non-commutative 3D rotation group SO(3).

Connected through hyperedge HE-1 (induced by source paper "learning group structure and disentangled representations of dynamical environments"):
  with members:
    - "hamiltonian generative networks"
    - "symmetry based disentangled representation learning requires interaction with environments"
    - "hamiltonian neural networks"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
    - "a style based generator architecture for generative adversarial networks"
    - "neural ordinary differential equations"
    - "towards a definition of disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "deferred neural rendering image synthesis using neural textures"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "video to video synthesis"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "layer structured 3d scene inference via view synthesis"
    - "ignor image guided neural object rendering"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-3 (induced by source paper "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "interactive inter layer activeness propagation"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-4 (induced by source paper "infogan cr disentangling generative adversarial networks with contrastive regularizers"):
  with members:
    - "infocatvae representation learning with categorical variational autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "disentangled vae representations for multi aspect and missing data"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised disentangled representation learning with analogical relations"

Connected through hyperedge HE-5 (induced by source paper "perturbations are not enough generating adversarial examples with spatial distortions"):
  with members:
    - "spatially transformed adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "rob gan generator discriminator and adversarial attacker"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "robustness of rotation equivariant networks to adversarial perturbations"
    - "improved network robustness with adversary critic"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "patch reordering a novel way to achieve rotation and translation invariance in convolutional neural networks"):
  with members:
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "locally scale invariant convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "understanding image representations by measuring their equivariance and equivalence"

Connected through hyperedge HE-7 (induced by source paper "scene representation networks continuous 3d structure aware neural scene representations"):
  with members:
    - "deepvoxels learning persistent 3d feature embeddings"
    - "consistent jumpy predictions for videos and scenes"
    - "progressive growing of gans for improved quality stability and variation"
    - "interpretable transformations with encoder decoder networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "conditional image generation with pixelcnn decoders"
    - "image to image translation with conditional adversarial networks"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised geometry aware representation for 3d human pose estimation"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning monocular 3d human pose estimation from multi view images"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "learning a multi view stereo machine"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

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)