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: "researchdoom and cocodoom learning computer vision with games"
  Abstract: In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game. We also introduce the CocoDoom dataset, a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. ResearchDoom and CocoDoom can be used to train and evaluate a variety of computer vision methods such as object recognition, detection and segmentation at the level of instances and categories, tracking, ego-motion estimation, monocular depth estimation and scene segmentation. The code and data are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "show attend and translate unsupervised image translation with self regularization and attention"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "the robotrix an extremely photorealistic and very large scale indoor dataset of sequences with robot trajectories and interactions"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"

Connected through hyperedge HE-3 (induced by source paper "scalenet guiding object proposal generation in supermarkets and beyond"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "instance sensitive fully convolutional networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "scale aware pixelwise object proposal networks"
    - "ssd single shot multibox detector"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-4 (induced by source paper "unsupervised pixel level domain adaptation with generative adversarial networks"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "perceptual losses for real time style transfer and super resolution"
    - "pixel level domain transfer"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "improved techniques for training gans"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

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)