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 "optimal representations for adaptive streaming in interactive multi view video systems"):
  Abstract: Interactive multi-view video streaming (IMVS) services permit to remotely immerse within a 3D scene. This is possible by transmitting a set of reference camera views (anchor views), which are used by the clients to freely navigate in the scene and possibly synthesize additional viewpoints of interest. From a networking perspective, the big challenge in IMVS systems is to deliver to each client the best set of anchor views that maximizes the navigation quality, minimizes the view-switching delay and yet satisfies the network constraints. Integrating adaptive streaming solutions in free-viewpoint systems offers a promising solution to deploy IMVS in large and heterogeneous scenarios, as long as the multi-view video representations on the server are properly selected. We therefore propose to optimize the multi-view data at the server by minimizing the overall resource requirements, yet offering a good navigation quality to the different users. We propose a video representation set optimization for multiview adaptive streaming systems and we show that it is NP-hard. We therefore introduce the concept of multi-view navigation segment that permits to cast the video representation set selection as an integer linear programming problem with a bounded computational complexity. We then show that the proposed solution reduces the computational complexity while preserving optimality in most of the 3D scenes. We then provide simulation results for different classes of users and show the gain offered by an optimal multi-view video representation selection compared to recommended representation sets (e.g., Netflix and Apple ones) or to a baseline representation selection algorithm where the encoding parameters are decided a priori for all the views.

Includes members:
  Member "merge frame design for video stream switching using piecewise constant functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed resolution image representation and compression with convolutional neural networks")
  Member "optimal lagrange multipliers for dependent rate allocation in video coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint denoising compression of image contours via shape prior and context tree")
  Member "in network view synthesis for interactive multiview video systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal multi view video transmission in multiuser wireless networks by exploiting natural and view synthesis enabled multicast opportunities")
  Member "optimal layered representation for adaptive interactive multiview video streaming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cache aided interactive multiview video streaming in small cell wireless networks")
  Member "optimal selection of adaptive streaming representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a knowledge driven quality of experience model for adaptive streaming videos")
  Member "optimized packet scheduling in multiview video navigation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimized data representation for interactive multiview navigation")

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)