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: "one packet suffices highly efficient packetized network coding with finite memory"
  Abstract: Random Linear Network Coding (RLNC) has emerged as a powerful tool for robust high-throughput multicast. Projection analysis - a recently introduced technique - shows that the distributed packetized RLNC protocol achieves (order) optimal and perfectly pipelined information dissemination in many settings. In the original approach to RNLC intermediate nodes code together all available information. This requires intermediate nodes to keep considerable data available for coding. Moreover, it results in a coding complexity that grows linearly with the size of this data. While this has been identified as a problem, approaches that combine queuing theory and network coding have heretofore not provided a succinct representation of the memory needs of network coding at intermediates nodes. #R##N#This paper shows the surprising result that, in all settings with a continuous stream of data, network coding continues to perform optimally even if only one packet per node is kept in active memory and used for computations. This leads to an extremely simple RLNC protocol variant with drastically reduced requirements on computational and memory resources. By extending the projection analysis, we show that in all settings in which the RLNC protocol was proven to be optimal its finite memory variant performs equally well. In the same way as the original projection analysis, our technique applies in a wide variety of network models, including highly dynamic topologies that can change completely at any time in an adversarial fashion.

Connected through hyperedge HE-1 (induced by source paper "secure multi source multicast"):
  with members:
    - "universal secure multiplex network coding with dependent and non uniform messages"
    - "sufficiently myopic adversaries are blind"
    - "probability of partially solving random linear systems in network coding"
    - "probability of partially decoding network coded messages"
    - "can network coding bridge the digital divide in the pacific"
    - "weakly secure regenerating codes for distributed storage"
    - "network coding capacity regions via entropy functions"
    - "universal secure network coding via rank metric codes"

Connected through hyperedge HE-2 (induced by source paper "lower bounds on information dissemination in dynamic networks"):
  with members:
    - "information spreading in dynamic networks"
    - "information spreading in dynamic graphs"
    - "information spreading in stationary markovian evolving graphs"
    - "faster information dissemination in dynamic networks via network coding"
    - "optimal gradient clock synchronization in dynamic networks"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-3 (induced by source paper "network coded gossip with correlated data"):
  with members:
    - "faster information dissemination in dynamic networks via network coding"
    - "order optimal information spreading using algebraic gossip"
    - "analyzing network coding gossip made easy"
    - "network information flow with correlated sources"

Connected through hyperedge HE-4 (induced by source paper "optimality of network coding in packet networks"):
  with members:
    - "analyzing network coding gossip made easy"
    - "algebraic gossip on arbitrary networks"
    - "arq for network coding"
    - "efficient operation of coded packet networks"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-5 (induced by source paper "analyzing network coding gossip made easy"):
  with members:
    - "optimal gossip with direct addressing"
    - "broadcast throughput in radio networks routing vs network coding"
    - "tight bounds for rumor spreading with vertex expansion"
    - "fast structuring of radio networks large for multi message communications"
    - "lower bounds on information dissemination in dynamic networks"
    - "bounded contention coding for wireless networks in the high snr regime"
    - "tighter worst case bounds on algebraic gossip"
    - "simple fast and deterministic gossip and rumor spreading"

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)