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 "intrinsic image popularity assessment"):
  Abstract: The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I$^2$PA) is established. We then develop computational models for I$^2$PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I$^2$PA.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "how to become instagram famous post popularity prediction with dual attention":
    also connected through other hyperedges:
      (none)
  Member "pay attention to virality understanding popularity of social media videos with the attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity")
  Member "sequential prediction of social media popularity with deep temporal context networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating early fundraising performance of innovations via graph based market environment model")
  Member "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the pushshift reddit dataset")
  Member "modeling image virality with pairwise spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memesequencer sparse matching for embedding image macros")
  Member "time matters multi scale temporalization of social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity")
  Member "dipiq blind image quality assessment by learning to rank discriminable image pairs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")

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.MM(Multimedia)