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: "decoding delay controlled reduction of completion time in instantly decodable network coding"
  Abstract: For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in persistent erasure channels. We first derive the decoding-delay-dependent expressions of the users' and overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we design two novel heuristics that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. We, then, extend our study to the limited feedback scenario. Simulation results show that our new algorithms achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.

Connected through hyperedge HE-1 (induced by source paper "rate aware instantly decodable network codes"):
  with members:
    - "on minimizing the maximum broadcast decoding delay for instantly decodable network coding"
    - "a game theoretic framework for decentralized cooperative data exchange using network coding"
    - "delay reduction in multi hop device to device communication using network coding"
    - "coding based data broadcasting for time critical applications with rate adaptation"
    - "completion time reduction in instantly decodable network coding through decoding delay control"
    - "a game theoretic approach to minimize the completion time of network coded cooperative data exchange"
    - "a lossy graph model for delay reduction in generalized instantly decodable network coding"
    - "partially blind instantly decodable network codes for lossy feedback environment"

Connected through hyperedge HE-2 (induced by source paper "delay reduction in multi hop device to device communication using network coding"):
  with members:
    - "fundamental green tradeoffs progresses challenges and impacts on 5g networks"
    - "data dissemination using instantly decodable binary codes in fog radio access networks"
    - "an overview of sustainable green 5g networks"
    - "rate aware instantly decodable network codes"
    - "on minimizing the maximum broadcast decoding delay for instantly decodable network coding"
    - "a game theoretic framework for decentralized cooperative data exchange using network coding"
    - "completion time reduction in instantly decodable network coding through decoding delay control"
    - "a game theoretic approach to minimize the completion time of network coded cooperative data exchange"

Connected through hyperedge HE-3 (induced by source paper "a game theoretic framework for decentralized cooperative data exchange using network coding"):
  with members:
    - "on minimizing the maximum broadcast decoding delay for instantly decodable network coding"
    - "completion time reduction in instantly decodable network coding through decoding delay control"
    - "a game theoretic approach to minimize the completion time of network coded cooperative data exchange"
    - "partially blind instantly decodable network codes for lossy feedback environment"
    - "delay minimization for instantly decodable network coding in persistent channels with feedback intermittence"
    - "reinforcement learning a survey"

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)