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: "systematic construction of anomaly detection benchmarks from real data"
  Abstract: Research in anomaly detection suffers from a lack of realistic and publicly-available data sets. Because of this, most published experiments in anomaly detection validate their algorithms with application-specific case studies or benchmark datasets of the researchers' construction. This makes it difficult to compare different methods or to measure progress in the field. It also limits our ability to understand the factors that determine the performance of anomaly detection algorithms. This article proposes a new methodology for empirical analysis and evaluation of anomaly detection algorithms. It is based on generating thousands of benchmark datasets by transforming existing supervised learning benchmark datasets and manipulating properties relevant to anomaly detection. The paper identifies and validates four important dimensions: (a) point difficulty, (b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d) relevance of features. We apply our generated datasets to analyze several leading anomaly detection algorithms. The evaluation verifies the importance of these dimensions and shows that, while some algorithms are clearly superior to others, anomaly detection accuracy is determined more by variation in the four dimensions than by the choice of algorithm.

Connected through hyperedge HE-1 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network"):
  with members:
    - "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"
    - "control barrier functions theory and applications"
    - "grasp2vec learning object representations from self supervised grasping"
    - "residual reinforcement learning for robot control"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "motion switching with sensory and instruction signals by designing dynamical systems using deep neural network"
    - "machine teaching a new paradigm for building machine learning systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-2 (induced by source paper "should i raise the red flag a comprehensive survey of anomaly scoring methods toward mitigating false alarms"):
  with members:
    - "active anomaly detection via ensembles"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep learning for time series analysis"
    - "a meta analysis of the anomaly detection problem"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-3 (induced by source paper "no free lunch but a cheaper supper a general framework for streaming anomaly detection"):
  with members:
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "a meta analysis of the anomaly detection problem"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "feature learning from incomplete eeg with denoising autoencoder"

Connected through hyperedge HE-4 (induced by source paper "finding skewed subcubes under a distribution"):
  with members:
    - "list decodable linear regression"
    - "learning from untrusted data"

Connected through hyperedge HE-5 (induced by source paper "pidforest anomaly detection via partial identification"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "openml networked science in machine learning"

Connected through hyperedge HE-6 (induced by source paper "deep anomaly detection with outlier exposure"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "an analysis of neural language modeling at multiple scales"
    - "exploring the limits of weakly supervised pretraining"
    - "discriminative out of distribution detection for semantic segmentation"
    - "learning to generate reviews and discovering sentiment"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-7 (induced by source paper "incorporating privileged information to unsupervised anomaly detection"):
  with members:
    - "building better detection with privileged information"
    - "extending detection with forensic information"
    - "patterns for learning with side information"
    - "learning to transfer privileged information"
    - "privileged information for data clustering"

Connected through hyperedge HE-8 (induced by source paper "are generative deep models for novelty detection truly better"):
  with members:
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "improved techniques for training gans"
    - "adam a method for stochastic optimization"

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.AI(Artificial Intelligence)