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 "identifying and compensating for feature deviation in imbalanced deep learning"):
  Abstract: We investigate learning a ConvNet classifier with class-imbalanced data. We found that a ConvNet over-fits significantly to the minor classes that do not have sufficient training instances, even if it is trained using vanilla ERM. We conduct a series of analysis and argue that feature deviation between the training and test instances serves as the main cause. We propose to incorporate class-dependent temperatures (CDT) in learning a ConvNet: CDT forces the minor-class instances to have larger decision values in training, so as to compensate for the effect of feature deviation in testing. We validate our approach on several benchmark datasets and achieve promising results. Our studies further suggest that class-imbalance data affects traditional machine learning and recent deep learning in very different ways. We hope that our insights can inspire new ways of thinking in resolving class-imbalanced deep learning.

Includes members:
  Member "a study on action detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decoupling representation and classifier for long tailed recognition")
  Member "simpleshot revisiting nearest neighbor classification for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "dynamic curriculum learning for imbalanced data classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "decoupling representation and classifier for long tailed recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "equalization loss for long tailed object recognition")
  Member "push the student to learn right progressive gradient correcting by meta learner on corrupted labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning imbalanced datasets with label distribution aware margin loss")
  Member "large scale long tailed recognition in an open world":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental few shot object detection")
  Member "class balanced loss based on effective number of samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equalization loss for long tailed object recognition")
  Member "meta weight net learning an explicit mapping for sample weighting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equalization loss for long tailed object recognition")

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.LG(Machine Learning)