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: "multi criteria anomaly detection using pareto depth analysis"
  Abstract: We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria using a linear combination of them. If the importance of the different criteria are not known in advance, the algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we introduce a novel non-parametric multi-criteria anomaly detection method using Pareto depth analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach scales linearly in the number of criteria and is provably better than linear combinations of the criteria.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection and classification for streaming data using pdes"):
  with members:
    - "multicriteria similarity based anomaly detection using pareto depth analysis"
    - "pareto depth for multiple query image retrieval"

Connected through hyperedge HE-2 (induced by source paper "multicriteria similarity based anomaly detection using pareto depth analysis"):
  with members:
    - "multi view learning in the presence of view disagreement"
    - "anomaly detection with score functions based on nearest neighbor graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "pareto depth for multiple query image retrieval"):
  with members:
    - "multi view learning in the presence of view disagreement"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-4 (induced by source paper "multi layer graph analysis for dynamic social networks"):
  with members:
    - "adaptive evolutionary clustering"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "online stochastic optimization with multiple objectives"):
  with members:
    - "learning from an optimization viewpoint"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "trading regret for efficiency online convex optimization with long term constraints"

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)