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: "specae spectral autoencoder for anomaly detection in attributed networks"
  Abstract: Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical scenarios, links describing instance-to-instance dependencies and interactions are available. Such systems are called attributed networks. Anomaly detection in attributed networks has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks. However, it remains a challenging task since the definition of anomaly becomes more complicated and topological structures are heterogeneous with nodal attributes. In this paper, we propose a spectral convolution and deconvolution based framework -- SpecAE, to project the attributed network into a tailored space to detect global and community anomalies. SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority. The learned representations along with reconstruction errors are combined with a density estimation model to perform the detection. They are trained jointly as an end-to-end framework. Experiments on real-world datasets demonstrate the effectiveness of SpecAE.

Connected through hyperedge HE-1 (induced by source paper "pyodds an end to end outlier detection system with automated machine learning"):
  with members:
    - "deep structured cross modal anomaly detection"
    - "pyod a python toolbox for scalable outlier detection"
    - "pyodds an end to end outlier detection system"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-2 (induced by source paper "deep structured cross modal anomaly detection"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "learning two branch neural networks for image text matching tasks"
    - "deep structured energy based models for anomaly detection"
    - "learning deep structure preserving image text embeddings"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-3 (induced by source paper "pyodds an end to end outlier detection system"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deep structured cross modal anomaly detection"
    - "pyod a python toolbox for scalable outlier detection"
    - "lstm based encoder decoder for multi sensor anomaly detection"

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)