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: "in network view synthesis for interactive multiview video systems"
  Abstract: To enable Interactive multiview video systems with a minimum view-switching delay, multiple camera views are sent to the users, which are used as reference images to synthesize additional virtual views via depth-image-based rendering. In practice, bandwidth constraints may however restrict the number of reference views sent to clients per time unit, which may in turn limit the quality of the synthesized viewpoints. We argue that the reference view selection should ideally be performed close to the users, and we study the problem of in-network reference view synthesis such that the navigation quality is maximized at the clients. We consider a distributed cloud network architecture where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. In order to satisfy last-hop bandwidth constraints from the cloudlet to the users, a cloudlet re-samples viewpoints of the 3D scene into a discrete set of views (combination of received camera views and virtual views synthesized) to be used as reference for the synthesis of additional virtual views at the client. This in-network synthesis leads to better viewpoint sampling given a bandwidth constraint compared to simple selection of camera views, but it may however carry a distortion penalty in the cloudlet-synthesized reference views. We therefore cast a new reference view selection problem where the best subset of views is defined as the one minimizing the distortion over a view navigation window defined by the user under some transmission bandwidth constraints. We show that the view selection problem is NP-hard, and propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem. Simulation results finally confirm the performance gain offered by virtual view synthesis in the network.

Connected through hyperedge HE-1 (induced by source paper "optimal multi view video transmission in multiuser wireless networks by exploiting natural and view synthesis enabled multicast opportunities"):
  with members:
    - "optimal multicast of tiled 360 vr video"
    - "adaptive streaming in interactive multiview video systems"
    - "energy efficient multi view video transmission with view synthesis enabled multicast"

Connected through hyperedge HE-2 (induced by source paper "had you looked where i m looking cross user similarities in viewing behavior for 360 video and caching implications"):
  with members:
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "optimized data representation for interactive multiview navigation"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-3 (induced by source paper "multi stream switching for interactive virtual reality video streaming"):
  with members:
    - "adaptive 360 vr video streaming based on mpeg dash srd"
    - "merge frame design for video stream switching using piecewise constant functions"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-4 (induced by source paper "optimal representations for adaptive streaming in interactive multi view video systems"):
  with members:
    - "merge frame design for video stream switching using piecewise constant functions"
    - "optimal lagrange multipliers for dependent rate allocation in video coding"
    - "optimal layered representation for adaptive interactive multiview video streaming"
    - "optimal selection of adaptive streaming representations"
    - "optimized packet scheduling in multiview video navigation systems"

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.MM(Multimedia)