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: "visalogy answering visual analogy questions"
  Abstract: In this paper, we study the problem of answering visual analogy questions. These questions take the form of image A is to image B as image C is to what. Answering these questions entails discovering the mapping from image A to image B and then extending the mapping to image C and searching for the image D such that the relation from A to B holds for C to D. We pose this problem as learning an embedding that encourages pairs of analogous images with similar transformations to be close together using convolutional neural networks with a quadruple Siamese architecture. We introduce a dataset of visual analogy questions in natural images, and show first results of its kind on solving analogy questions on natural images.

Connected through hyperedge HE-1 (induced by source paper "detecting unseen visual relations using analogies"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "large scale visual relationship understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "a simple neural network module for relational reasoning"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "relnet end to end modeling of entities relations"

Connected through hyperedge HE-2 (induced by source paper "learning to forecast videos of human activity with multi granularity models and adaptive rendering"):
  with members:
    - "learning to generate long term future via hierarchical prediction"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the pose knows video forecasting by generating pose futures"
    - "stylebank an explicit representation for neural image style transfer"
    - "unsupervised learning for physical interaction through video prediction"
    - "perceptual losses for real time style transfer and super resolution"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "deep feature interpolation for image content changes"):
  with members:
    - "neural photo editing with introspective adversarial networks"
    - "learning a predictable and generative vector representation for objects"
    - "energy based generative adversarial network"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "adversarial feature learning"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-4 (induced by source paper "modeling context in referring expressions"):
  with members:
    - "lstm a search space odyssey"
    - "ssd single shot multibox detector"
    - "grounding of textual phrases in images by reconstruction"
    - "generation and comprehension of unambiguous object descriptions"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deepm a deep part based model for object detection and semantic part localization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "contextual visual similarity"):
  with members:
    - "automatic attribute discovery with neural activations"
    - "disentangling nonlinear perceptual embeddings with multi query triplet networks"
    - "conditional similarity networks"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "unsupervised learning of visual representations using videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-6 (induced by source paper "from a to z supervised transfer of style and content using deep neural network generators"):
  with members:
    - "a neural algorithm of artistic style"
    - "deep convolutional inverse graphics network"
    - "semi supervised learning with deep generative models"
    - "distributed representations of words and phrases and their compositionality"
    - "theano new features and speed improvements"

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)