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: "optimal selection of adaptive streaming representations"
  Abstract: Adaptive streaming addresses the increasing and heterogeneous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) is characterized by a resolution and a bit rate, and it is aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-buffer control strategies on the client side, in this article we take a providers' perspective and propose solutions to improve user satisfaction by optimizing the set of available representations. We formulate an integer linear program that maximizes users' average satisfaction, taking into account network dynamics, type of video content, and user population characteristics. The solution of the optimization is a set of encoding parameters corresponding to the representations set that maximizes user satisfaction. We evaluate this solution by simulating multiple adaptive streaming sessions characterized by realistic network statistics, showing that the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness among users and to reduce network resource usage. We finally propose a few theoretical guidelines that can be used, in realistic settings, to choose the encoding parameters based on the user characteristics, the network capacity and the type of video content.

Connected through hyperedge HE-1 (induced by source paper "a knowledge driven quality of experience model for adaptive streaming videos"):
  with members:
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "learning to predict streaming video qoe distortions rebuffering and memory"
    - "bola near optimal bitrate adaptation for online videos"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-2 (induced by source paper "multiuser video streaming rate adaptation a physical layer resource aware deep reinforcement learning approach"):
  with members:
    - "non cooperative game theory based rate adaptation for dynamic video streaming over http"
    - "asynchronous methods for deep reinforcement learning"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-3 (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"
    - "in network view synthesis for interactive multiview video systems"
    - "optimal layered representation for adaptive interactive multiview video streaming"
    - "optimized packet scheduling in multiview video navigation systems"

Connected through hyperedge HE-4 (induced by source paper "prioritized random mac optimization via graph based analysis"):
  with members:
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "seek and decode random access with physical layer network coding and multiuser detection"
    - "coded random access how coding theory helps to build random access protocols"
    - "exploiting capture effect in frameless aloha for massive wireless random access"
    - "what will 5g be"
    - "cooperative slotted aloha for multi base station systems"
    - "the throughput of slotted aloha with diversity"
    - "aloha random access that operates as a rateless code"

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)