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: "towards practical file packetizations in wireless device to device caching networks"
  Abstract: We consider wireless device-to-device (D2D) caching networks with single-hop transmissions. Previous work has demonstrated that caching and coded multicasting can significantly increase per user throughput. However, the state-of-the-art coded caching schemes for D2D networks are generally impractical because content files are partitioned into an exponential number of packets with respect to the number of users if both library and memory sizes are fixed. In this paper, we present two novel combinatorial approaches to coded caching design in D2D networks with the goal of reducing the required packetization while keeping the desired throughput gain compared to the conventional uncoded unicasting. The first proposed approach is based on a novel "hypercube" design, where each user caches a "hyperplane" in this hypercube and the intersections of "hyperplanes" lead to coded multicasting codewords. In addition, we also extend this centralized scheme to a decentralized design. The second approach uses the Ruzsa-Szemeredi graph to define the cache placement and disjoint matchings on this graph represent coded multicasting opportunities. Both approaches yield an exponential reduction of packetizations with respect to the number of users while providing a per-user throughput that is comparable to the state-of-the-art designs in the literature. Furthermore, by using the spatial reuse in D2D networks, besides further reducing the required packetizations, we demonstrate that the per user throughput can be improved significantly for some parameter regimes.

Connected through hyperedge HE-1 (induced by source paper "cache aided interference management using hypercube combinatorial cache designs"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"
    - "physical layer schemes for wireless coded caching"
    - "degrees of freedom of cache aided wireless interference networks"
    - "on caching with more users than files"
    - "finite length analysis of caching aided coded multicasting"
    - "cache aided interference channels"

Connected through hyperedge HE-2 (induced by source paper "a new design framework on device to device coded caching with optimal rate and significantly less subpacketizations"):
  with members:
    - "device to device private caching with trusted server"
    - "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery"
    - "information theoretic analysis of d2d aided pipelined content delivery in fog ran"
    - "on the placement delivery array design for centralized coded caching scheme"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"
    - "on the placement delivery array design for coded caching scheme in d2d networks"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "centralized coded caching schemes a hypergraph theoretical approach"

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)