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: "robust face recognition via multimodal deep face representation"
  Abstract: Face images appearing in multimedia applications, e.g., social networks and digital entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in considerable performance degradation for traditional face recognition algorithms. This paper proposes a comprehensive deep learning framework to jointly learn face representation using multimodal information. The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE). The set of CNNs extracts complementary facial features from multimodal data. Then, the extracted features are concatenated to form a high-dimensional feature vector, whose dimension is compressed by SAE. All of the CNNs are trained using a subset of 9,000 subjects from the publicly available CASIA-WebFace database, which ensures the reproducibility of this work. Using the proposed single CNN architecture and limited training data, 98.43% verification rate is achieved on the LFW database. Benefitting from the complementary information contained in multimodal data, our small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.

Connected through hyperedge HE-1 (induced by source paper "face off adversarial face obfuscation"):
  with members:
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "generating adversarial examples with adversarial networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mitigating evasion attacks to deep neural networks via region based classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-2 (induced by source paper "pi gan learning pose independent representations for multiple profile face synthesis"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep convolutional inverse graphics network"
    - "facenet a unified embedding for face recognition and clustering"
    - "a comprehensive survey on pose invariant face recognition"

Connected through hyperedge HE-3 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "selective sensor fusion for neural visual inertial odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "lo net deep real time lidar odometry"
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"

Connected through hyperedge HE-5 (induced by source paper "fully learnable group convolution for acceleration of deep neural networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "learning transferable architectures for scalable image recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "representation learning by rotating your faces"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "additive margin softmax for face verification"
    - "nonlinear 3d face morphable model"
    - "began boundary equilibrium generative adversarial networks"
    - "pose invariant face alignment with a single cnn"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-7 (induced by source paper "a capsule unified framework of deep neural networks for graphical programming"):
  with members:
    - "a unified framework of deep neural networks by capsules"
    - "dynamic routing between capsules"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "densely connected convolutional networks"
    - "an overview of gradient descent optimization algorithms"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "sigan siamese generative adversarial network for identity preserving face hallucination"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "surveillance face recognition challenge"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"
    - "pixel recursive super resolution"
    - "learning to hallucinate face images via component generation and enhancement"
    - "deep joint face hallucination and recognition"

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)