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: "residual codean autoencoder for facial attribute analysis"
  Abstract: Facial attributes can provide rich ancillary information which can be utilized for different applications such as targeted marketing, human computer interaction, and law enforcement. This research focuses on facial attribute prediction using a novel deep learning formulation, termed as R-Codean autoencoder. The paper first presents Cosine similarity based loss function in an autoencoder which is then incorporated into the Euclidean distance based autoencoder to formulate R-Codean. The proposed loss function thus aims to incorporate both magnitude and direction of image vectors during feature learning. Further, inspired by the utility of shortcut connections in deep models to facilitate learning of optimal parameters, without incurring the problem of vanishing gradient, the proposed formulation is extended to incorporate shortcut connections in the architecture. The proposed R-Codean autoencoder is utilized in facial attribute prediction framework which incorporates patch-based weighting mechanism for assigning higher weights to relevant patches for each attribute. The experimental results on publicly available CelebA and LFWA datasets demonstrate the efficacy of the proposed approach in addressing this challenging problem.

Connected through hyperedge HE-1 (induced by source paper "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "single shot temporal action detection"
    - "single shot text detector with regional attention"
    - "scale aware face detection"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-2 (induced by source paper "supervised cosmos autoencoder learning beyond the euclidean loss"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "relational autoencoder for feature extraction"
    - "improving facial attribute prediction using semantic segmentation"
    - "dynamic routing between capsules"
    - "age progression regression by conditional adversarial autoencoder"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

Connected through hyperedge HE-3 (induced by source paper "anonymizing k facial attributes via adversarial perturbations"):
  with members:
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "facial attributes accuracy and adversarial robustness"
    - "are facial attributes adversarially robust"
    - "towards evaluating the robustness of neural networks"
    - "multi task cnn model for attribute prediction"
    - "deep learning face attributes in the wild"

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)