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 "adding transmitters dramatically boosts coded caching gains for finite file sizes"):
  Abstract: In the context of coded caching in the $K$-user BC, our work reveals the surprising fact that having multiple ($L$) transmitting antennas, dramatically ameliorates the long-standing subpacketization bottleneck of coded caching by reducing the required subpacketization to approximately its $L$th root, thus boosting the actual DoF by a multiplicative factor of up to $L$. In asymptotic terms, this reveals that as long as $L$ scales with the theoretical caching gain, then the full cumulative (multiplexing + full caching) gains are achieved with constant subpacketization. This is the first time, in any known setting, that unbounded caching gains appear under finite file-size constraints. The achieved caching gains here are up to $L$ times higher than any caching gains previously experienced in any single- or multi-antenna fully-connected setting, thus offering a multiplicative mitigation to a subpacketization problem that was previously known to hard-bound caching gains to small constants. #R##N#The proposed scheme is practical and it works for all values of $K,L$ and all cache sizes. The scheme's gains show in practice: e.g. for $K=100$, when $L=1$ the theoretical caching gain of $G=10$, under the original coded caching algorithm, would have needed subpacketization $S_1 = \binom{K}{G}= \binom{100}{10} > 10^{13}$, while if extra transmitting antennas were added, the subpacketization was previously known to match or exceed $S_1$. Now for $L=5$, our scheme offers the theoretical (unconstrained) cumulative DoF $d_L = L+G = 5+10=15$, with subpacketization $S_L=\binom{K/L}{G/L} =\binom{100/5}{10/5} = 190$. The work extends to the multi-server and cache-aided IC settings, while the scheme's performance, given subpacketization $S_L=\binom{K/L}{G/L}$, is within a factor of 2 from the optimal linear sum-DoF.

Includes members:
  Member "the exact rate memory tradeoff for caching with uncoded prefetching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "coded distributed computing with node cooperation substantially increases speedup factors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a fundamental storage communication tradeoff in distributed computing with straggling nodes")
  Member "on the optimality of separation between caching and delivery in general cache networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asynchronous coded caching")
  Member "on the placement delivery array design for centralized coded caching scheme":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "interference networks with caches at both ends":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "degrees of freedom of cache aided wireless interference networks")
  Member "on coded caching in the overloaded miso broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "multi antenna coded caching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to code coded caching via deep reinforcement learning")

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)