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: "one class classification taxonomy of study and review of techniques"
  Abstract: One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/ novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.

Connected through hyperedge HE-1 (induced by source paper "one class classification by ensembles of regression models a detailed study"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "one class kernel spectral regression for outlier detection"
    - "detecting unseen falls from wearable devices using channel wise ensemble of autoencoders"

Connected through hyperedge HE-2 (induced by source paper "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "open world learning and application to product classification"
    - "efficient large scale domain classification with personalized attention"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "learning from positive and unlabeled data a survey"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"

Connected through hyperedge HE-3 (induced by source paper "detecting out of distribution inputs in deep neural networks using an early layer output"):
  with members:
    - "learning deep features for one class classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal adversarial learning for detecting unseen falls"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "adversarial spatio temporal learning for video deblurring"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "satellite image forgery detection and localization using gan and one class classifier"
    - "adversarially learned one class classifier for novelty detection"
    - "deepfall non invasive fall detection with deep spatio temporal convolutional autoencoders"

Connected through hyperedge HE-5 (induced by source paper "one class convolutional neural network"):
  with members:
    - "learning deep features for one class classification"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "active authentication using an autoencoder regularized cnn based one class classifier"):
  with members:
    - "one class convolutional neural network"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection using one class neural networks"
    - "abnormal event detection in videos using generative adversarial nets"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "convolutional neural networks for attribute based active authentication on mobile devices"
    - "a learned representation for artistic style"

Connected through hyperedge HE-7 (induced by source paper "one way prototypical networks"):
  with members:
    - "one class convolutional neural network"
    - "deep learning for anomaly detection a survey"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "gaussian prototypical networks for few shot learning on omniglot"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "adepos a novel approximate computing framework for anomaly detection systems and its implementation in 65nm cmos"):
  with members:
    - "a stacked autoencoder neural network based automated feature extraction method for anomaly detection in on line condition monitoring"
    - "neuron pruning for compressing deep networks using maxout architectures"
    - "adam a method for stochastic optimization"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "an autoencoder approach to learning bilingual word representations"
    - "2013 special issue learning the pseudoinverse solution to network weights"

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.LG(Machine Learning)