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 "using cnns for users segmentation in video see through augmented virtuality"):
  Abstract: In this paper, we present preliminary results on the use of deep learning techniques to integrate the user's self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing user's bodies in such simulations may improve the feeling of both self and social presence in the virtual environment, as well as user performance. We propose to use a convolutional neural network for real time semantic segmentation of users' bodies in the stereoscopic RGB video streams acquired from the perspective of the user. We describe design issues as well as implementation details of the system and demonstrate the feasibility of using such neural networks for merging users' bodies in an augmented virtuality simulation.

Includes members:
  Member "dfanet deep feature aggregation for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "fast scnn fast semantic segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerating object detection by erasing background activations")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "fast and robust hand tracking using detection guided optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")
  Member "microsoft coco common objects in context":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)