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: "visualizing object detection features"
  Abstract: We introduce algorithms to visualize feature spaces used by object detectors. Our method works by inverting a visual feature back to multiple natural images. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and supports that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of recognition systems.

Connected through hyperedge HE-1 (induced by source paper "on the reconstruction of face images from deep face templates"):
  with members:
    - "pixel deconvolutional networks"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "srefi synthesis of realistic example face images"
    - "improved training of wasserstein gans"
    - "synthesizing normalized faces from facial identity features"
    - "memnet a persistent memory network for image restoration"
    - "clustering millions of faces by identity"

Connected through hyperedge HE-2 (induced by source paper "featureless bypassing feature extraction in action categorization"):
  with members:
    - "deep learning in neural networks"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "p cnn pose based cnn features for action recognition"
    - "a discriminative cnn video representation for event detection"
    - "two stream convolutional networks for action recognition in videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-3 (induced by source paper "discussion among different methods of updating model filter in object tracking"):
  with members:
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving human activity recognition from extreme low resolution"):
  with members:
    - "studying very low resolution recognition using deep networks"
    - "deeply recursive convolutional network for image super resolution"

Connected through hyperedge HE-5 (induced by source paper "anticipating visual representations from unlabeled video"):
  with members:
    - "unsupervised visual representation learning by context prediction"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "unsupervised learning of visual representations using videos"
    - "dense optical flow prediction from a static image"
    - "understanding deep image representations by inverting them"
    - "going deeper with convolutions"
    - "video language modeling a baseline for generative models of natural videos"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-6 (induced by source paper "anticipating the future by watching unlabeled video"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "understanding deep image representations by inverting them"
    - "going deeper with convolutions"
    - "video language modeling a baseline for generative models of natural videos"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"

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)