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 "anchor loss modulating loss scale based on prediction difficulty"):
  Abstract: We propose a novel loss function that dynamically rescales the cross entropy based on prediction difficulty regarding a sample. Deep neural network architectures in image classification tasks struggle to disambiguate visually similar objects. Likewise, in human pose estimation symmetric body parts often confuse the network with assigning indiscriminative scores to them. This is due to the output prediction, in which only the highest confidence label is selected without taking into consideration a measure of uncertainty. In this work, we define the prediction difficulty as a relative property coming from the confidence score gap between positive and negative labels. More precisely, the proposed loss function penalizes the network to avoid the score of a false prediction being significant. To demonstrate the efficacy of our loss function, we evaluate it on two different domains: image classification and human pose estimation. We find improvements in both applications by achieving higher accuracy compared to the baseline methods.

Includes members:
  Member "multi scale structure aware network for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "smooth loss functions for deep top k classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "imae for noise robust learning mean absolute error does not treat examples equally and gradient magnitude s variance matters")
  Member "learning to reweight examples for robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "generalized cross entropy loss for training deep neural networks with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "using trusted data to train deep networks on labels corrupted by severe noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "benchmarking and error diagnosis in multi instance pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "arbee towards automated recognition of bodily expression of emotion in the wild")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)