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: "max margin class imbalanced learning with gaussian affinity"
  Abstract: Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an `affinity measure' in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.

Connected through hyperedge HE-1 (induced by source paper "debface de biasing face recognition"):
  with members:
    - "generative adversarial minority oversampling"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "probabilistic face embeddings"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"
    - "minimax entropy network learning category invariant features for domain adaptation"
    - "class balanced loss based on effective number of samples"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multi task convolutional neural network for pose invariant face recognition"

Connected through hyperedge HE-2 (induced by source paper "decoupling representation and classifier for long tailed recognition"):
  with members:
    - "a study on action detection in the wild"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "striking the right balance with uncertainty"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning to reweight examples for robust deep learning"
    - "additive margin softmax for face verification"
    - "linear maximum margin classifier for learning from uncertain data"
    - "feature transfer learning for deep face recognition with under represented data"
    - "vggface2 a dataset for recognising faces across pose and age"

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)