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: "compositional gan learning image conditional binary composition"
  Abstract: Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose a novel self-consistent Composition-by-Decomposition (CoDe) network to compose a pair of objects. Given object images from two distinct distributions, our model can generate a realistic composite image from their joint distribution following the texture and shape of the input objects. We evaluate our approach through qualitative experiments and user evaluations. Our results indicate that the learned model captures potential interactions between the two object domains, and generates realistic composed scenes at test time.

Connected through hyperedge HE-1 (induced by source paper "adaptive composition gan towards realistic image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "data augmentation for object detection via progressive and selective instance switching"):
  with members:
    - "data augmentation using random image cropping and patching for deep cnns"
    - "dynamic curriculum learning for imbalanced data classification"
    - "class balanced loss based on effective number of samples"
    - "learning to segment via cut and paste"
    - "data augmentation by pairing samples for images classification"
    - "on the importance of visual context for data augmentation in scene understanding"
    - "compositional gan learning conditional image composition"
    - "modeling visual context is key to augmenting object detection datasets"

Connected through hyperedge HE-3 (induced by source paper "semantic bottleneck scene generation"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "semantic image synthesis with spatially adaptive normalization"
    - "specifying object attributes and relations in interactive scene generation"
    - "high fidelity image generation with fewer labels"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "self supervised gans via auxiliary rotation loss"
    - "neural text generation past present and beyond"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "laso label set operations networks for multi label few shot learning"):
  with members:
    - "compositional gan learning conditional image composition"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "deep meta learning learning to learn in the concept space"
    - "the elephant in the room"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"
    - "multi label zero shot learning with structured knowledge graphs"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "genesis generative scene inference and sampling with object centric latent representations"):
  with members:
    - "emergence of object segmentation in perturbed generative models"
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised object segmentation by redrawing"

Connected through hyperedge HE-6 (induced by source paper "unconstrained foreground object search"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "generative image inpainting with contextual attention"
    - "billion scale similarity search with gpus"
    - "where and who automatic semantic aware person composition"
    - "seeing what is not there learning context to determine where objects are missing"
    - "deep image harmonization"

Connected through hyperedge HE-7 (induced by source paper "investigating object compositionality in generative adversarial networks"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "emergence of object segmentation in perturbed generative models"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "unsupervised object segmentation by redrawing"
    - "object discovery with a copy pasting gan"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the gan landscape losses architectures regularization and normalization"

Connected through hyperedge HE-8 (induced by source paper "spatial fusion gan for image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "esir end to end scene text recognition via iterative image rectification"
    - "fast end to end trainable guided filter"
    - "deep painterly harmonization"

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)