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: "coding based data broadcasting for time critical applications with rate adaptation"
  Abstract: In this paper, we dynamically select the transmission rate and design wireless network coding to improve the quality of services, such as delay for time-critical applications. In a network coded system, with a low transmission rate and, hence, a longer transmission range, more packets may be encoded, which increases the coding opportunity. However, a low transmission rate may incur extra transmission delay, which is intolerable for time-critical applications. We design a novel joint rate selection and wireless network coding (RSNC) scheme with a delay constraint to maximize the total benefit (where we can define the benefit based on the priority or importance of a packet for example) of the packets that are successfully received at the destinations without missing their deadlines. We prove that the proposed problem is NP-hard and propose a novel graph model to mathematically formulate the problem. For the general case, we propose a transmission metric and design an efficient algorithm to determine the transmission rate and coding strategy for each transmission. For a special case when all delay constraints are the same, we study pairwise coding and present a polynomial-time pairwise coding algorithm that achieves an approximation ratio of    $\hbox{1} -(\hbox{1}/e)$   to the optimal pairwise coding solution, where    $e$   is the base of the natural logarithm. Finally, simulation results demonstrate the superiority of the proposed RSNC scheme.

Connected through hyperedge HE-1 (induced by source paper "a survey on resource allocation in vehicular networks"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "radio resource allocation for reliable out of coverage v2v communications"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "deep reinforcement learning a brief survey"
    - "vehicular communications a network layer perspective"
    - "an end to end network slicing framework for 5g wireless communication systems"
    - "reinforcement learning for resource provisioning in the vehicular cloud"
    - "dynamic resource allocation in cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "instantly decodable network coding for real time scalable video broadcast over wireless networks"):
  with members:
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "adaptive prioritized random linear coding and scheduling for layered data delivery from multiple servers"
    - "fulcrum network codes a code for fluid allocation of complexity"
    - "random linear network coding for wireless layered video broadcast general design methods for adaptive feedback free transmission"
    - "partially blind instantly decodable network codes for lossy feedback environment"
    - "network coding meets multimedia a review"
    - "joint coding and scheduling optimization in wireless systems with varying delay sensitivities"
    - "completion delay minimization for instantly decodable network codes"

Connected through hyperedge HE-3 (induced by source paper "approaching single hop performance in multihop networks end to end known interference cancelation e2e kic"):
  with members:
    - "distributed mac protocol supporting physical layer network coding"
    - "blind known interference cancellation"
    - "coordinating interfering transmissions in cooperative wireless lans"
    - "pushing the limits of full duplex design and real time implementation"

Connected through hyperedge HE-4 (induced by source paper "rate aware instantly decodable network codes"):
  with members:
    - "on minimizing the maximum broadcast decoding delay for instantly decodable network coding"
    - "decoding delay controlled reduction of completion time in 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"
    - "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"

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)