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: "learning deep features for one class classification"
  Abstract: We present a novel deep-learning-based approach for  one-class transfer learning  in which labeled data from an unrelated task is used for feature learning in one-class classification. The proposed method operates on top of a convolutional neural network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions,  compactness loss  and  descriptiveness loss,  are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection, and mobile active authentication datasets show that the proposed deep one-class (DOC) classification method achieves significant improvements over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "deep learning achieves perfect anomaly detection on 108 308 retinal images including unlearned diseases"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mixup beyond empirical risk minimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "domain adaptive attention model for unsupervised cross domain person re identification"):
  with members:
    - "harmonious attention network for person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"
    - "in defense of the triplet loss for person re identification"
    - "camera style adaptation for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-3 (induced by source paper "deep cnn based multi task learning for open set recognition"):
  with members:
    - "open category detection with pac guarantees"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "and autoregressive novelty detectors"
    - "learning from noisy web data with category level supervision"
    - "classification reconstruction learning for open set recognition"
    - "the extreme value machine"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-4 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering networks"

Connected through hyperedge HE-5 (induced by source paper "detecting out of distribution inputs in deep neural networks using an early layer output"):
  with members:
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"
    - "deep bayesian active learning with image data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "multi stage deep classifier cascades for open world recognition"):
  with members:
    - "robust regression via online feature selection under adversarial data corruption"
    - "open world learning and application to product classification"
    - "random warping series a random features method for time series embedding"
    - "progressive neural networks"
    - "open world visual recognition using knowledge graphs"
    - "similarity preserving representation learning for time series clustering"
    - "icarl incremental classifier and representation learning"
    - "online open world recognition"

Connected through hyperedge HE-7 (induced by source paper "one class convolutional neural network"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "improved techniques for training gans"
    - "a learned representation for artistic style"

Connected through hyperedge HE-8 (induced by source paper "gods generalized one class discriminative subspaces for anomaly detection"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "subspace support vector data description"
    - "anomaly detection using one class neural networks"
    - "video representation learning using discriminative pooling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "one class slab support vector machine"

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)