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: "learning representations of ultrahigh dimensional data for random distance based outlier detection"
  Abstract: Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).    This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO.    Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement.

Connected through hyperedge HE-1 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "deep semi supervised anomaly detection"
    - "efficient training for positive unlabeled learning"
    - "adversarially learned anomaly detection"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-2 (induced by source paper "outlier detection ensemble with embedded feature selection"):
  with members:
    - "robust graph learning from noisy data"
    - "similarity learning via kernel preserving embedding"
    - "feature selection a data perspective"
    - "sequential ensemble learning for outlier detection a bias variance perspective"

Connected through hyperedge HE-3 (induced by source paper "unsupervised representation learning by predicting random distances"):
  with members:
    - "exploration by random network distillation"
    - "unsupervised representation learning by predicting image rotations"
    - "representation learning with contrastive predictive coding"
    - "unsupervised learning of depth and ego motion from video"
    - "unsupervised representation learning by sorting sequences"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "context encoders feature learning by inpainting"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-4 (induced by source paper "deep anomaly detection with deviation networks"):
  with members:
    - "efficient training for positive unlabeled learning"
    - "adversarially learned anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised deep anomaly detection with pairwise relation learning"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"
    - "toward supervised 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)