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: "anomaly detection using autoencoders in high performance computing systems"
  Abstract: Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states).We propose a novel approach for anomaly detection in HighPerformance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with).We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).

Connected through hyperedge HE-1 (induced by source paper "free riders in federated learning attacks and defenses"):
  with members:
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "analyzing federated learning through an adversarial lens"
    - "dancing in the dark private multi party machine learning in an untrusted setting"
    - "security and privacy issues in deep learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated learning"

Connected through hyperedge HE-2 (induced by source paper "peek inside the closed world evaluating autoencoder based detection of ddos to cloud"):
  with members:
    - "one class convolutional neural network"
    - "machine learning ddos detection for consumer internet of things devices"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "anomaly detection using one class neural networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "adam a method for stochastic optimization"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-3 (induced by source paper "a pvalue guided anomaly detection approach combining multiple heterogeneous log parser algorithms on iiot systems"):
  with members:
    - "a directed acyclic graph approach to online log parsing"
    - "tools and benchmarks for automated log parsing"
    - "plug in martingales for testing exchangeability on line"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-4 (induced by source paper "latent space conditioning for improved classification and anomaly detection"):
  with members:
    - "gee a gradient based explainable variational autoencoder for network anomaly detection"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "unsupervised deep embedding for clustering analysis"

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)