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: "reliability of broadcast communications under sparse random linear network coding"
  Abstract: Ultrareliable point-to-multipoint communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse random linear network coding techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kind of coding technique. In particular, by exploiting the Stein–Chen method, we provide a novel and general performance framework applicable to any combination of system and service parameters, such as finite field sizes, lengths of the data stream, and level of sparsity. The deviation of the proposed approximation from Monte Carlo simulations is negligible, improving significantly on the state-of-the-art performance bounds.

Connected through hyperedge HE-1 (induced by source paper "on the probability of partial decoding in sparse network coding"):
  with members:
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "probability of partially decoding network coded messages"
    - "binary systematic network coding for progressive packet decoding"
    - "feedback based online network coding"
    - "intermediate performance of rateless codes"

Connected through hyperedge HE-2 (induced by source paper "random linear network coding for 5g mobile video delivery"):
  with members:
    - "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"
    - "random linear network coding for wireless layered video broadcast general design methods for adaptive feedback free transmission"

Connected through hyperedge HE-3 (induced by source paper "on intercept probability minimization under sparse random linear network coding"):
  with members:
    - "reliability of multicast under random linear network coding"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "rethinking the intercept probability of random linear network coding"

Connected through hyperedge HE-4 (induced by source paper "reliability of multicast under random linear network coding"):
  with members:
    - "decoding delay performance of random linear network coding for broadcast"
    - "novel performance analysis of network coded communications in single relay networks"
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "binary systematic network coding for progressive packet decoding"
    - "optimized network coded scalable video multicasting over embms networks"
    - "performance analysis of random linear network coding in two source single relay networks"
    - "rethinking the intercept probability of random linear network coding"
    - "network coding meets multimedia a review"

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)