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 "non volume preserving based feature fusion approach to group level expression recognition on crowd videos"):
  Abstract: Group-level emotion recognition (ER) is a growing research area as the demands for assessing crowds of all sizes is becoming an interest in both the security arena and social media. This work investigates group-level expression recognition on crowd videos where information is not only aggregated across a variable length sequence of frames but also over the set of faces within each frame to produce aggregated recognition results. In this paper, we propose an effective deep feature level fusion mechanism to model the spatial-temporal information in the crowd videos. Furthermore, we extend our proposed NVP fusion mechanism to temporal NVP fussion appoarch to learn the temporal information between frames. In order to demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on the AffectNet database to benchmark the proposed emoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii) examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos (GECV) dataset composed of 627 videos collected from social media. GECV dataset is a collection of videos ranging in duration from 10 to 20 seconds of crowds of twenty (20) or more subjects and each video is labeled as positive, negative, or neutral.

Includes members:
  Member "affectnet a database for facial expression valence and arousal computing in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "an attention model for group level emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi scale guided attention for medical image segmentation")
  Member "mobilefacenets efficient cnns for accurate real time face verification on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond")
  Member "temporal non volume preserving approach to facial age progression and age invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "group level emotion recognition using transfer learning from face identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet")

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)