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: "logan unpaired shape transform in latent overcomplete space"
  Abstract: We present LOGAN, a deep neural network aimed at learning generic shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and chairs, but there is neither a pairing between shapes in the two domains to supervise the shape translation nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes encode multi-scale shape features in an overcomplete manner. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a feature preservation loss ensures that the right shape features are preserved for a natural shape transform. We conduct various ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that our network is able to learn what shape features to preserve during shape transforms, either local or non-local, whether content or style, etc., depending solely on the input domain pairs.

Connected through hyperedge HE-1 (induced by source paper "ganhopper multi hop gan for unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "carigans unpaired photo to caricature translation"
    - "multimodal unsupervised image to image translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "samplenet differentiable point cloud sampling"):
  with members:
    - "adaptive hierarchical down sampling for point cloud classification"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "unsupervised detection of distinctive regions on 3d shapes"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pcrnet point cloud registration network using pointnet encoding"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "unpaired point cloud completion on real scans using adversarial training"

Connected through hyperedge HE-3 (induced by source paper "cross domain cascaded deep feature translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "neural best buddies sparse cross domain correspondence"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "improving shape deformation in unsupervised image to image translation"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-4 (induced by source paper "bsp net generating compact meshes via binary space partitioning"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "bae net branched autoencoder for shape co segmentation"
    - "sdm net deep generative network for structured deformable mesh"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"

Connected through hyperedge HE-5 (induced by source paper "neural cages for detail preserving 3d deformations"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "3dn 3d deformation network"
    - "unsupervised cycle consistent deformation for shape matching"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "sdm net deep generative network for structured deformable mesh"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "3d coded 3d correspondences by deep deformation"
    - "learning free form deformations for 3d object reconstruction"

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.CV(Computer Vision and Pattern Recognition)