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 capacity of secure distributed matrix multiplication"):
  Abstract: The problem of secure distributed matrix multiplication (SDMM) studies the communication efficiency of retrieving a sequence of desired $L\times M$ matrix products ${\bf AB}=({\bf A}_1{\bf B}_1,{\bf A_2B_2}, \cdots,{\bf A}_S{\bf B}_S)$ from $N$ distributed servers where the constituent $L\times K$ matrices ${\bf A}=({\bf A}_1, {\bf A}_2, \cdots, {\bf A}_S)$ and $K\times M$ matrices ${\bf B}=({\bf B}_1, {\bf B}_2,\cdots,{\bf B}_S)$ are stored in $X$-secure coded form, i.e., any group of up to $X$ colluding servers learn nothing about ${\bf A, B}$. The rate of an SDMM scheme is defined as the ratio of the number of bits of desired information that is retrieved, to the total number of bits downloaded on average. The supremum of achievable rates is called the capacity of SDMM. In this work we explore the capacity of SDMM, as well as several of its variants, e.g., where the user may already have either ${\bf A}$ or ${\bf B}$ available as side-information, and/or where the security constraint for either ${\bf A}$ or ${\bf B}$ may be relaxed. As our main contribution, we obtain new converse bounds, as well as new achievable schemes for various cases of SDMM, depending on the $L, K, M, N, X$ parameters, and identify parameter regimes where these bounds match. A remarkable aspect of our upper bounds is a connection between SDMM and a form of private information retrieval (PIR) problem, known as multi-message $X$-secure $T$-private information retrieval (MM-XSTPIR). Notable features of our achievable schemes include the use of cross-subspace alignment and a transformation argument that converts a scalar multiplication problem into a scalar addition problem, allowing a surprisingly efficient solution.

Includes members:
  Member "the capacity of private information retrieval from uncoded storage constrained databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent variable private information retrieval")
  Member "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "single server multi message private information retrieval with side information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private computation with individual and joint privacy")
  Member "on the capacity of secure distributed matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "fundamental limits of cache aided private information retrieval with unknown and uncoded prefetching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent variable private information retrieval")
  Member "the capacity of private information retrieval with private side information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private computation with individual and joint privacy")
  Member "the capacity of cache aided private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "latent variable private 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)