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: "optimal control for network coding broadcast"
  Abstract: Random linear network coding (RLNC) has been shown to efficiently improve the network performance in terms of reducing transmission delays and increasing the throughput in broadcast and multicast communications. However, it can result in increased storage and computational complexity at the receivers end. In our previous work we considered the broadcast transmission of large file to N receivers. We showed that the storage and complexity requirements at the receivers end can be greatly reduced when segmenting the file into smaller blocks and applying RLNC to these blocks. To that purpose, we proposed a packet scheduling policy, namely the Least Received. In this work we will prove the optimality of our previously proposed policy, in terms of file transfer completion time, when N = 2. We will model our system as a Markov Decision Process and prove the optimality of the policy using Dynamic Programming. Our intuition is that the Least Received policy may be optimal regardless of the number of receivers. Towards that end, we will provide experimental results that verify that ntuition.

Connected through hyperedge HE-1 (induced by source paper "delay complexity trade off of random linear network coding in wireless broadcast"):
  with members:
    - "circular shift linear network coding"
    - "circular shift linear network codes with arbitrary odd block lengths"
    - "approximating throughput and packet decoding delay in linear network coded wireless broadcast"
    - "decoding delay performance of random linear network coding for broadcast"
    - "on vector linear solvability of multicast networks"
    - "decoding and file transfer delay balancing in network coding broadcast"
    - "completion delay minimization for instantly decodable network codes"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"

Connected through hyperedge HE-2 (induced by source paper "delay optimal scheduling for chunked random linear network coding broadcast"):
  with members:
    - "throughput of rateless codes over broadcast erasure channels"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"
    - "sparse network coding with overlapping classes"

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.SY(Systems and Control)