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 from ambiguously labeled face images"
  Abstract: Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled face. We further extend MCar to incorporate the labeling constraints between instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvement on several ambiguously labeled datasets.

Connected through hyperedge HE-1 (induced by source paper "network cooperation with progressive disambiguation for partial label learning"):
  with members:
    - "partial label learning with self guided retraining"
    - "learning with inadequate and incorrect supervision"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "understanding deep learning requires rethinking generalization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "progressive identification of true labels for partial label learning"):
  with members:
    - "partial label learning with self guided retraining"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "hera partial label learning by combining heterogeneous loss with sparse and low rank regularization"):
  with members:
    - "partial label learning with self guided retraining"
    - "a regularization approach for instance based superset label learning"
    - "robust recovery of subspace structures by low rank representation"
    - "large scale multi label learning with missing labels"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-4 (induced by source paper "adversarial partial multi label learning"):
  with members:
    - "unsupervised learning of object landmarks through conditional image generation"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "gm pll graph matching based partial label learning"):
  with members:
    - "a regularization approach for instance based superset label learning"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-6 (induced by source paper "partial label learning with self guided retraining"):
  with members:
    - "a regularization approach for instance based superset label learning"

Connected through hyperedge HE-7 (induced by source paper "side information for face completion a robust pca approach"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-8 (induced by source paper "a self paced regularization framework for partial label learning"):
  with members:
    - "a regularization approach for instance based superset label learning"
    - "self paced deep learning for weakly supervised object detection"
    - "what objective does self paced learning indeed optimize"
    - "solving multiclass learning problems via error correcting output codes"

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.CV(Computer Vision and Pattern Recognition)