human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model"):
  Abstract: In this paper, we address the problem of how to robustly train a ConvNet for regression, or deep robust regression. Traditionally, deep regression employs the L2 loss function, known to be sensitive to outliers, i.e. samples that either lie at an abnormal distance away from the majority of the training samples, or that correspond to wrongly annotated targets. This means that, during back-propagation, outliers may bias the training process due to the high magnitude of their gradient. In this paper, we propose DeepGUM: a deep regression model that is robust to outliers thanks to the use of a Gaussian-uniform mixture model. We derive an optimization algorithm that alternates between the unsupervised detection of outliers using expectation-maximization, and the supervised training with cleaned samples using stochastic gradient descent. DeepGUM is able to adapt to a continuously evolving outlier distribution, avoiding to manually impose any threshold on the proportion of outliers in the training set. Extensive experimental evaluations on four different tasks (facial and fashion landmark detection, age and head pose estimation) lead us to conclude that our novel robust technique provides reliability in the presence of various types of noise and protection against a high percentage of outliers.

Includes members:
  Member "a comprehensive analysis of deep regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cahphf context aware hierarchical qos prediction with hybrid filtering")
  Member "viraliency pooling local virality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integrating text and image determining multimodal document intent in instagram posts")
  Member "how to make an image more memorable a deep style transfer approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ganalyze toward visual definitions of cognitive image properties")
  Member "robust head pose estimation based on partially latent mixture of linear regressions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "laeo net revisiting people looking at each other in videos")
  Member "learning from noisy labels with distillation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "em algorithms for weighted data clustering with application to audio visual scene analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers")
  Member "fashion landmark detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two stream aural visual affect analysis in the wild")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)