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 "instance enhancement batch normalization an adaptive regulator of batch noise"):
  Abstract: Batch Normalization (BN) (Ioffe and Szegedy 2015) normalizes the features of an input image via statistics of a batch of images and this batch information is considered as batch noise that will be brought to the features of an instance by BN. We offer a point of view that self-attention mechanism can help regulate the batch noise by enhancing instance-specific information. Based on this view, we propose combining BN with a self-attention mechanism to adjust the batch noise and give an attention-based version of BN called Instance Enhancement Batch Normalization (IEBN) which recalibrates channel information by a simple linear transformation. IEBN outperforms BN with a light parameter increment in various visual tasks universally for different network structures and benchmark data sets. Besides, even if under the attack of synthetic noise, IEBN can still stabilize network training with good generalization. The code of IEBN is available at this https URL

Includes members:
  Member "a quantitative analysis of the effect of batch normalization on gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implicit regularization of normalization methods")
  Member "instance level meta normalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attentive normalization")
  Member "spatial group wise enhance improving semantic feature learning in convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dnanet de normalized attention based multi resolution network for human pose estimation")
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a metric learning reality check")
  Member "selective kernel networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "dianet dense and implicit attention network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "channel attention with embedding gaussian process a probabilistic methodology")
  Member "group normalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "differentiable learning to normalize via switchable normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adapted center and scale prediction more stable and more accurate")

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)