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: "cortexnet a generic network family for robust visual temporal representations"
  Abstract: In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks. These models have achieved super-human performance on object recognition, localisation, and detection in still images. However, there is a need to identify the best strategy to employ these networks with temporal visual inputs and obtain a robust and stable representation of video data. Inspired by the human visual system, we propose a deep neural network family, CortexNet, which features not only bottom-up feed-forward connections, but also it models the abundant top-down feedback and lateral connections, which are present in our visual cortex. We introduce two training schemes - the unsupervised MatchNet and weakly supervised TempoNet modes - where a network learns how to correctly anticipate a subsequent frame in a video clip or the identity of its predominant subject, by learning egomotion clues and how to automatically track several objects in the current scene. Find the project website at this https URL.

Connected through hyperedge HE-1 (induced by source paper "toyarchitecture unsupervised learning of interpretable models of the world"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a neurally inspired hierarchical prediction network for spatiotemporal sequence learning and prediction"
    - "unsupervised predictive memory in a goal directed agent"
    - "the consciousness prior"
    - "a simple neural network module for relational reasoning"
    - "dynamic routing between capsules"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-2 (induced by source paper "integrating motion into vision models for better visual prediction"):
  with members:
    - "image transformation can make neural networks more robust against adversarial examples"
    - "saccadic predictive vision model with a fovea"
    - "unsupervised learning from continuous video in a scalable predictive recurrent network"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "recurrent models of visual attention"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "maximum likelihood approximate nearest neighbor method in real time image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "viplfacenet an open source deep face recognition sdk"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised visual instrument playing action detection in videos"):
  with members:
    - "look listen and learn"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "see hear and read deep aligned representations"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "audio event detection using weakly labeled data"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-5 (induced by source paper "deep predictive coding network for object recognition"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "squeeze and excitation networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "recurrent ladder networks"):
  with members:
    - "semi supervised phoneme recognition with recurrent ladder networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "neural expectation maximization"
    - "on the exact relationship between the denoising function and the data distribution"
    - "unsupervised learning for physical interaction through video prediction"
    - "stacked hourglass networks for human pose estimation"
    - "learning recurrent representations for hierarchical behavior modeling"
    - "feynman machine the universal dynamical systems computer"

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)