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: "random linear network coding for wireless layered video broadcast general design methods for adaptive feedback free transmission"
  Abstract: This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold. First, to systematically investigate the benefits of UEP+RLNC layered approach in servicing users with different reception capabilities. Second, to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study `expected percentage of decoded frames' as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.

Connected through hyperedge HE-1 (induced by source paper "network coding based evolutionary network formation for dynamic wireless networks"):
  with members:
    - "decoding delay minimization in inter session network coding"
    - "distributed rate allocation in inter session network coding"
    - "enabling wireless power transfer in cellular networks architecture modeling and deployment"
    - "mac centered cooperation synergistic design of network coding multi packet reception and improved fairness to increase network throughput"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-2 (induced by source paper "random linear network coding for 5g mobile video delivery"):
  with members:
    - "reliability of broadcast communications under sparse random linear network coding"
    - "reliable video streaming over mmwave with multi connectivity and network coding"
    - "decoding delay performance of random linear network coding for broadcast"
    - "optimized interface diversity for ultra reliable low latency communication urllc"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "collaborative mobile edge computing in 5g networks new paradigms scenarios and challenges"
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "optimized network coded scalable video multicasting over embms networks"

Connected through hyperedge HE-3 (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"
    - "coding based data broadcasting for time critical applications with rate adaptation"
    - "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-4 (induced by source paper "network coding for video distortion reduction in device to device communications"):
  with members:
    - "adaptive prioritized random linear coding and scheduling for layered data delivery from multiple servers"
    - "content aware instantly decodable network coding over wireless networks"
    - "delay reduction in multi hop device to device communication using network coding"
    - "data dissemination in wireless networks with network coding"
    - "coded cooperative data exchange in multihop networks"
    - "coded cooperative data exchange problem for general topologies"
    - "joint coding and scheduling optimization in wireless systems with varying delay sensitivities"
    - "completion delay minimization for instantly decodable network codes"

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)