human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "modeling image virality with pairwise spatial transformer networks"
  Abstract: The study of virality and information diffusion online is a topic gaining traction rapidly in the computational social sciences. Computer vision and social network analysis research have also focused on understanding the impact of content and information diffusion in making content viral, with prior approaches not performing significantly well as other traditional classification tasks. In this paper, we present a novel pairwise reformulation of the virality prediction problem as an attribute prediction task and develop a novel algorithm to model image virality on online media using a pairwise neural network. Our model provides significant insights into the features that are responsible for promoting virality and surpasses the existing state-of-the-art by a 12% average improvement in prediction. We also investigate the effect of external category supervision on relative attribute prediction and observe an increase in prediction accuracy for the same across several attribute learning datasets.

Connected through hyperedge HE-1 (induced by source paper "intrinsic image popularity assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "how to become instagram famous post popularity prediction with dual attention"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "time matters multi scale temporalization of social media popularity"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "predicting popularity of online videos using support vector regression"

Connected through hyperedge HE-2 (induced by source paper "memesequencer sparse matching for embedding image macros"):
  with members:
    - "information evolution in social networks"
    - "do cascades recur"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "end to end localization and ranking for relative attributes"
    - "tensorflow a system for large scale machine learning"
    - "understanding image virality"
    - "skip thought vectors"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "pairwise confusion for fine grained visual classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generalized orderless pooling performs implicit salient matching"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "densely connected convolutional networks"
    - "deep learning the city quantifying urban perception at a global scale"
    - "weakly supervised fine grained categorization with part based image representation"
    - "end to end localization and ranking for relative attributes"

Question: Please tell me which class the center node 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)