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: "anomaly detection with score functions based on nearest neighbor graphs"
  Abstract: We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on score functions derived from nearest neighbor graphs on $n$-point nominal data. Anomalies are declared whenever the score of a test sample falls below $\alpha$, which is supposed to be the desired false alarm level. The resulting anomaly detector is shown to be asymptotically optimal in that it is uniformly most powerful for the specified false alarm level, $\alpha$, for the case when the anomaly density is a mixture of the nominal and a known density. Our algorithm is computationally efficient, being linear in dimension and quadratic in data size. It does not require choosing complicated tuning parameters or function approximation classes and it can adapt to local structure such as local change in dimensionality. We demonstrate the algorithm on both artificial and real data sets in high dimensional feature spaces.

Connected through hyperedge HE-1 (induced by source paper "network representation learning consolidation and renewed bearing"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "pl nmf parallel locality optimized non negative matrix factorization"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mile a multi level framework for scalable graph embedding"
    - "verse versatile graph embeddings from similarity measures"
    - "fusion graph convolutional networks"
    - "systematic biases in link prediction comparing heuristic and graph embedding based methods"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-2 (induced by source paper "fixing bias in reconstruction based anomaly detection with lipschitz discriminators"):
  with members:
    - "a survey on gans for anomaly detection"
    - "deep double descent where bigger models and more data hurt"
    - "memorization in overparameterized autoencoders"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-3 (induced by source paper "online multivariate anomaly detection and localization for high dimensional settings"):
  with members:
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "concept drift and anomaly detection in graph streams"

Connected through hyperedge HE-4 (induced by source paper "real time nonparametric anomaly detection in high dimensional settings"):
  with members:
    - "secure distributed dynamic state estimation in wide area smart grids"
    - "real time detection of hybrid and stealthy cyber attacks in smart grid"
    - "multisensor sequential change detection with unknown change propagation pattern"
    - "online cyber attack detection in smart grid a reinforcement learning approach"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-5 (induced by source paper "denoising autoencoders for overgeneralization in neural networks"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "energy based generative adversarial network"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "towards open set deep networks"
    - "towards open world recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-6 (induced by source paper "robust deep and inductive anomaly detection"):
  with members:
    - "deep structured energy based models for anomaly detection"
    - "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"
    - "adam a method for stochastic optimization"
    - "learning efficient sparse and low rank models"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-7 (induced by source paper "multicriteria similarity based anomaly detection using pareto depth analysis"):
  with members:
    - "multi view learning in the presence of view disagreement"
    - "multi criteria anomaly detection using pareto depth analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "nonparametric detection of anomalous data via kernel mean embedding"):
  with members:
    - "universal outlier hypothesis testing"
    - "a kernel method for the two sample problem"

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)