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 "fundamental limits of cache aided private information retrieval with unknown and uncoded prefetching"):
  Abstract: We consider the problem of private information retrieval (PIR) from $N$ non-colluding and replicated databases when the user is equipped with a cache that holds an uncoded fraction $r$ from each of the $K$ stored messages in the databases. We assume that the databases are unaware of the cache content. We investigate $D^*(r)$ the optimal download cost normalized with the message size as a function of $K$, $N$, $r$. For a fixed $K$, $N$, we develop an inner bound (converse bound) for the $D^*(r)$ curve. The inner bound is a piece-wise linear function in $r$ that consists of $K$ line segments. For the achievability, we develop explicit schemes that exploit the cached bits as side information to achieve $K-1$ non-degenerate corner points. These corner points differ in the number of cached bits that are used to generate one side information equation. We obtain an outer bound (achievability) for any caching ratio by memory-sharing between these corner points. Thus, the outer bound is also a piece-wise linear function in $r$ that consists of $K$ line segments. The inner and the outer bounds match in general for the cases of very low caching ratio ($r \leq \frac{1}{1+N+N^2+\cdots+N^{K-1}}$) and very high caching ratio ($r \geq \frac{K-2}{(N+1)K+N^2-2N-2}$). As a corollary, we fully characterize the optimal download cost caching ratio tradeoff for $K=3$. For general $K$, $N$, and $r$, we show that the largest gap between the achievability and the converse bounds is $\frac{1}{6}$. Our results show that the download cost can be reduced beyond memory-sharing if the databases are unaware of the cached content.

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 "private information retrieval from mds coded data in distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent variable private information retrieval")
  Member "a general private information retrieval scheme for mds coded databases with colluding servers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private set intersection a multi message symmetric private information retrieval perspective")
  Member "multi file private information retrieval from mds coded databases with colluding servers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent variable private information retrieval")
  Member "the capacity of cache aided private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent variable private information retrieval")
  Member "the capacity of private information retrieval from byzantine and colluding databases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "private information retrieval from mds coded data with colluding servers settling a conjecture by freij hollanti et al":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent variable private information retrieval")
  Member "private information retrieval schemes for coded data with arbitrary collusion patterns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noisy private information retrieval on separability of channel coding and information retrieval")

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)