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: "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.

Connected through hyperedge HE-1 (induced by source paper "cross layer scheduling and beamforming in smart grid powered cellular networks with heterogeneous energy coordination"):
  with members:
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "robust energy efficient beamforming in misome swipt systems with proportional secrecy rate"
    - "dynamic cross layer beamforming in hybrid powered communication systems with harvest use trade strategy"
    - "a hybrid energy sharing framework for green cellular networks"
    - "an overview of sustainable green 5g networks"
    - "ergodic energy management leveraging resource variability in distribution grids"
    - "comp meets smart grid a new communication and energy cooperation paradigm"
    - "power delay tradeoff with predictive scheduling in integrated cellular and wi fi networks"

Connected through hyperedge HE-2 (induced by source paper "pa cache learning based popularity aware content caching in edge networks"):
  with members:
    - "online deep learning learning deep neural networks on the fly"
    - "what when and where to cache a unified optimization approach"
    - "optimizing timer based policies for general cache networks"
    - "understanding performance of edge content caching for mobile video streaming"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "fractalnet ultra deep neural networks without residuals"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "finite sample analysis of decentralized temporal difference learning with linear function approximation"):
  with members:
    - "reanalysis of variance reduced temporal difference learning"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "two timescale voltage control in distribution grids using deep reinforcement learning"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "finite time performance bounds and adaptive learning rate selection for two time scale reinforcement learning"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-4 (induced by source paper "real time voltage control using deep reinforcement learning"):
  with members:
    - "autonomous voltage control for grid operation using deep reinforcement learning"
    - "real time power system state estimation and forecasting via deep unrolled neural networks"
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "voltage regulation algorithms for multiphase power distribution grids"
    - "ergodic energy management leveraging resource variability in distribution grids"

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.IT(Information Theory)