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: "generative adversarial active learning for unsupervised outlier detection"
  Abstract: Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio.

Connected through hyperedge HE-1 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "pyod a python toolbox for scalable outlier detection"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "are generative deep models for novelty detection truly better"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-2 (induced by source paper "rcc dual gan an efficient approach for outlier detection with few identified anomalies"):
  with members:
    - "heartbeat anomaly detection using adversarial oversampling"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-3 (induced by source paper "suod a scalable unsupervised outlier detection framework"):
  with members:
    - "suod toward scalable unsupervised outlier detection"
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "pyod a python toolbox for scalable outlier detection"
    - "optimal sparse decision trees"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "semi supervised rare disease detection using generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "outlier guided optimization of abdominal segmentation"):
  with members:
    - "splenomegaly segmentation on multi modal mri using deep convolutional networks"
    - "unsupervised body part regression via spatially self ordering convolutional neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"

Connected through hyperedge HE-5 (induced by source paper "a flexible outlier detector based on a topology given by graph communities"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "anomaly detection using one class neural networks"

Connected through hyperedge HE-6 (induced by source paper "graph adversarial training dynamically regularizing based on graph structure"):
  with members:
    - "neural graph collaborative filtering"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial training towards robust multimedia recommender system"
    - "aaane attention based adversarial autoencoder for multi scale network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adversarial personalized ranking for recommendation"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-7 (induced by source paper "pyod a python toolbox for scalable outlier detection"):
  with members:
    - "dcso dynamic combination of detector scores for outlier ensembles"
    - "anomaly detection for an e commerce pricing system"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-8 (induced by source paper "active learning for event detection in support of disaster analysis applications"):
  with members:
    - "automatic detection of passable roads after floods in remote sensed and social media data"
    - "natural disasters detection in social media and satellite imagery a survey"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image 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.LG(Machine Learning)