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: "recurrent neural radio anomaly detection"
  Abstract: We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomalies in highly complex complexity multi-user radio bands. We demonstrate the efficacy of this approach on a number of common real over the air radio communications bands of interest and quantify detection performance in terms of probability of detection an false alarm rates across a range of interference to band power ratios and compare to baseline methods.

Connected through hyperedge HE-1 (induced by source paper "lstm based anomaly detection detection rules from extreme value theory"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
    - "anomaly detection for a water treatment system using unsupervised machine learning"

Connected through hyperedge HE-2 (induced by source paper "learning to optimize training deep neural networks for interference management"):
  with members:
    - "detection algorithms for communication systems using deep learning"
    - "deep learning based mimo communications"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "why deep neural networks for function approximation"
    - "signal processing and optimal resource allocation for the interference channel"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-3 (induced by source paper "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"):
  with members:
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "generating sentences from a continuous space"
    - "denoising criterion for variational auto encoding framework"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "reaching in clutter with whole arm tactile sensing"

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)