human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the packet decoding delay of linear network coded wireless broadcast"):
  Abstract: We apply linear network coding (LNC) to broadcast a block of data packets from one sender to a set of receivers via lossy wireless channels, assuming each receiver already possesses a subset of these packets and wants the rest. We aim to characterize the average packet decoding delay (APDD), which reflects how soon each individual data packet can be decoded by each receiver on average, and to minimize it while achieving optimal throughput. To this end, we first derive closed-form lower bounds on the expected APDD of all LNC techniques under random packet erasures. We then prove that these bounds are NP-hard to achieve and, thus, that APDD minimization is an NP-hard problem. We then study the performance of some existing LNC techniques, including random linear network coding (RLNC) and instantly decodable network coding (IDNC). We proved that all throughput-optimal LNC techniques can approximate the minimum expected APDD with a ratio between 4/3 and 2. In particular, the ratio of RLNC is exactly 2. We then prove that all IDNC techniques are only heuristics in terms of throughput optimization and {cannot guarantee an APDD approximation ratio for at least a subset of the receivers}. Finally, we propose hyper-graphic linear network coding (HLNC), a novel throughput-optimal and APDD-approximating LNC technique based on a hypergraph model of receivers' packet reception state. We implement it under different availability of receiver feedback, and numerically compare its performance with RLNC and a heuristic general IDNC technique. The results show that the APDD performance of HLNC is better under all tested system settings, even if receiver feedback is only collected intermittently.

Includes members:
  Member "performance characterization and transmission schemes for instantly decodable network coding in wireless broadcast":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating throughput and packet decoding delay in linear network coded wireless broadcast")
  Member "on deterministic linear network coded broadcast and its relation to matroid theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "device to device networking meets cellular via network coding")
  Member "completion delay minimization for instantly decodable network codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coalition formation game for delay reduction in instantly decodable network coding assisted d2d communications")
  Member "on coding for cooperative data exchange":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "embedded index coding")
  Member "broadcasting in time division duplexing a random linear network coding approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "packet loss recovery in broadcast for real time applications in dense wireless networks")
  Member "informed network coding for minimum decoding delay":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "packet loss recovery in broadcast for real time applications in dense wireless networks")
  Member "on the index coding problem and its relation to network coding and matroid theory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient 3d road map data exchange for intelligent vehicles in vehicular fog networks")

Question: Please tell me which class the source paper 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)