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: "a survey on social media anomaly detection"
  Abstract: Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large amount of work have been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection. In this paper, we present a survey on existing approaches to address this problem. We focus on the new type of anomalous phenomena in the social media and review the recent developed techniques to detect those special types of anomalies. We provide a general overview of the problem domain, common formulations, existing methodologies and potential directions. With this work, we hope to call out the attention from the research community on this challenging problem and open up new directions that we can contribute in the future.

Connected through hyperedge HE-1 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "how to escape saddle points efficiently"
    - "unregularized online learning algorithms with general loss functions"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "temporal relational ranking for stock prediction"):
  with members:
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "neural collaborative filtering"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "unsupervised learning of video representations using lstms"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "group anomaly detection using deep generative models"):
  with members:
    - "learning deep features for one class classification"
    - "real world anomaly detection in surveillance videos"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "detecting topological changes in dynamic community networks"):
  with members:
    - "the resistance perturbation distance a metric for the analysis of dynamic networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "communities and hierarchical structures in dynamic social networks analysis and visualization"
    - "anomaly detection in dynamic networks of varying size"
    - "exact recovery in the stochastic block model"
    - "stochastic block transition models for dynamic networks"
    - "intrinsically dynamic network communities"

Connected through hyperedge HE-5 (induced by source paper "outlier edge detection using random graph generation models and applications"):
  with members:
    - "limited random walk algorithm for big graph data clustering"
    - "graph based anomaly detection and description a survey"
    - "graph degree linkage agglomerative clustering on a directed graph"
    - "clustering with multi layer graphs a spectral perspective"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"
    - "a classification for community discovery methods in complex networks"

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)