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: "deep structured cross modal anomaly detection"
  Abstract: Anomaly detection is a fundamental problem in data mining field with many real-world applications. A vast majority of existing anomaly detection methods predominately focused on data collected from a single source. In real-world applications, instances often have multiple types of features, such as images (ID photos, finger prints) and texts (bank transaction histories, user online social media posts), resulting in the so-called multi-modal data. In this paper, we focus on identifying anomalies whose patterns are disparate across different modalities, i.e., cross-modal anomalies. Some of the data instances within a multi-modal context are often not anomalous when they are viewed separately in each individual modality, but contains inconsistent patterns when multiple sources are jointly considered. The existence of multi-modal data in many real-world scenarios brings both opportunities and challenges to the canonical task of anomaly detection. On the one hand, in multi-modal data, information of different modalities may complement each other in improving the detection performance. On the other hand, complicated distributions across different modalities call for a principled framework to characterize their inherent and complex correlations, which is often difficult to capture with conventional linear models. To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data. Experiments on real-world datasets demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "pyodds an end to end outlier detection system with automated machine learning"):
  with members:
    - "specae spectral autoencoder for anomaly detection in attributed networks"
    - "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 "history based anomaly detector an adversarial approach to anomaly detection"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "a survey on gans for anomaly detection"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "deep learning for anomaly detection a survey"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "learning rich features for image manipulation detection"

Connected through hyperedge HE-3 (induced by source paper "specae spectral autoencoder for anomaly detection in attributed networks"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-4 (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"
    - "specae spectral autoencoder for anomaly detection in attributed networks"
    - "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)