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 "evaluating the progress of deep learning for visual relational concepts"):
  Abstract: Convolutional Neural Networks (CNNs) have become the state of the art method for image classification in the last 7 years, but despite the fact that they achieve super human performance on many classification datasets, there are lesser known datasets where they almost fail completely and perform much worse than humans. #R##N#We will show that these problems correspond to relational concepts as defined by the field of concept learning. Therefore, we will present current deep learning research for visual relational concepts. #R##N#Analyzing the current literature, we will hypothesise that iterative processing of the input, together with shifting attention between the iterations will be needed to efficiently and reliably solve real world relational concept learning. In addition, we will conclude that many current datasets overestimate the performance of tested systems by providing data in an already pre-attended form.

Includes members:
  Member "v prom a benchmark for visual reasoning using visual progressive matrices":
    also connected through other hyperedges:
      (none)
  Member "same different problems strain convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "tips and tricks for visual question answering learnings from the 2017 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "in defense of grid features for visual question answering")
  Member "iq of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical rule induction network for abstract visual reasoning")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "a study and comparison of human and deep learning recognition performance under visual distortions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")

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)