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: "deep transfer learning for multiple class novelty detection"
  Abstract: We propose a transfer learning-based solution for the problem of multiple class novelty detection. In particular, we propose an end-to-end deep-learning based approach in which we investigate how the knowledge contained in an external, out-of-distributional dataset can be used to improve the performance of a deep network for visual novelty detection. Our solution differs from the standard deep classification networks on two accounts. First, we use a novel loss function, membership loss, in addition to the classical cross-entropy loss for training networks. Secondly, we use the knowledge from the external dataset more effectively to learn globally negative filters, filters that respond to generic objects outside the known class set. We show that thresholding the maximal activation of the proposed network can be used to identify novel objects effectively. Extensive experiments on four publicly available novelty detection datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "multi class novelty detection using mix up technique"):
  with members:
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "hierarchical novelty detection for visual object recognition"
    - "metric learning for novelty and anomaly detection"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (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"
    - "learning deep features for one class classification"
    - "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-3 (induced by source paper "learning deep features for one class classification"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "latent space autoregression for novelty detection"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "sparse representation based open set recognition"
    - "active user authentication for smartphones a challenge data set and benchmark results"

Connected through hyperedge HE-4 (induced by source paper "uninformed students student teacher anomaly detection with discriminative latent embeddings"):
  with members:
    - "improving unsupervised defect segmentation by applying structural similarity to autoencoders"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "do better imagenet models transfer better"
    - "informed democracy voting based novelty detection for action recognition"
    - "are pre trained cnns good feature extractors for anomaly detection in surveillance videos"
    - "latent space autoregression for novelty detection"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "segregation network for multi class novelty detection"):
  with members:
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "metric learning for novelty and anomaly detection"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "generative openmax for multi class open set classification"
    - "mixup beyond empirical risk minimization"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-6 (induced by source paper "ocgan one class novelty detection using gans with constrained latent representations"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "and autoregressive novelty detectors"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-7 (induced by source paper "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"):
  with members:
    - "deep cnn based multi task learning for open set recognition"
    - "deep multimodal subspace clustering networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep coral correlation alignment for deep domain adaptation"

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)