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 "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"):
  Abstract: Caching is envisioned to play a critical role in next-generation content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning framework is put forth. To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued. The novel approach relies on a deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.

Includes members:
  Member "joint switch upgrade and controller deployment in hybrid software defined networks":
    also connected through other hyperedges:
      (none)
  Member "bandit online learning with unknown delays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits")
  Member "optimal and scalable caching for 5g using reinforcement learning of space time popularities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online convex optimization for caching networks")
  Member "applications of deep reinforcement learning in communications and networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "deep reinforcement learning based mode selection and resource management for green fog radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications")
  Member "decentralized caching for content delivery based on blockchain a game theoretic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the design of dynamic probabilistic caching with time varying content popularity")
  Member "a reinforcement learning approach to proactive caching in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online convex optimization for caching networks")
  Member "deep reinforcement learning a brief survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")

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.IT(Information Theory)