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: "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"
  Abstract: Accurate cross-layer information is very useful for optimizing and monitoring mobile networks for specific applications. However, the wide adoption of end-to-end encryption and the absence of common standards has made it very difficult to obtain such information by deep packet inspection (DPI) or cross-layer signaling. In this paper, we present a traffic profiling system as an alternative solution for HTTP adaptive streaming (HAS) traffic. By observing IP packet flows, our machine learning system detects the video flows as well as the play-back buffer state of an HAS client in real time. Studying 5 classification methods with an extensive dataset for YouTube's mobile client, shows very high accuracy even with a strong variation of link quality. Since this high performance is achieved for a generic feature set at IP level, our approach requires no DPI, works equally with TCP and UDP, and does not interfere with end-to-end encryption. Traffic profiling is, thus, a powerful new tool for managing even encrypted HAS traffic in mobile networks.

Connected through hyperedge HE-1 (induced by source paper "optimizing adaptive video streaming in mobile networks via online learning"):
  with members:
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "multi codec dash dataset"
    - "a comparative case study of http adaptive streaming algorithms in mobile networks"
    - "http adaptive streaming with indoors outdoors detection in mobile networks"
    - "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 "the role of caching in future communication systems and networks"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "on coding for cache aided delivery of dynamic correlated content"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "updating content in cache aided coded multicast"
    - "on the effects of subpacketization in content centric mobile networks"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a reinforcement learning approach to proactive caching in wireless networks"

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)