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 conditional image composition"
  Abstract: Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism, but are generally modeled 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 to model object composition in a GAN framework as a self-consistent composition-decomposition network. Our model is conditioned on the object images from their marginal distributions to generate a realistic image from their joint distribution by explicitly learning the possible interactions. We evaluate our model through qualitative experiments and user evaluations in both the scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training. Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion.

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"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spatial fusion gan for image synthesis"

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"
    - "modeling visual context is key to augmenting object detection datasets"
    - "revisiting pre training an efficient training method for image classification"

Connected through hyperedge HE-3 (induced by source paper "laso label set operations networks for multi label few shot learning"):
  with members:
    - "attributes as operators factorizing unseen attribute object compositions"
    - "deep meta learning learning to learn in the concept space"
    - "the elephant in the room"
    - "compositional gan learning image conditional binary composition"
    - "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-4 (induced by source paper "unconstrained foreground object search"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning image conditional binary 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-5 (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"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "esir end to end scene text recognition via iterative image rectification"
    - "compositional gan learning image conditional binary composition"
    - "fast end to end trainable guided filter"
    - "deep painterly harmonization"

Connected through hyperedge HE-6 (induced by source paper "instance level facial attributes transfer with geometry aware flow"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unsupervised image to image translation networks"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-7 (induced by source paper "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping"):
  with members:
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "towards end to end lane detection an instance segmentation approach"
    - "cross view image synthesis using geometry guided conditional gans"
    - "compositional gan learning image conditional binary composition"
    - "generative adversarial frontal view to bird view synthesis"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-8 (induced by source paper "componet learning to generate the unseen by part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "scores shape composition with recursive substructure priors"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "began boundary equilibrium generative adversarial networks"
    - "multi generator generative adversarial nets"

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)