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: "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.

Connected through hyperedge HE-1 (induced by source paper "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "distributed and private coded matrix computation with flexible communication load"
    - "secure coded multi party computation for massive matrix operations"
    - "private and secure distributed matrix multiplication with flexible communication load"
    - "private secure coded computation"
    - "harmonic coding an optimal linear code for privacy preserving gradient type computation"
    - "uplink downlink tradeoff in secure distributed matrix multiplication"
    - "on the upload versus download cost for secure and private matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "notes on communication and computation in secure distributed matrix multiplication"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "gasp codes for secure distributed matrix multiplication"
    - "one shot pir refinement and lifting"
    - "on the capacity of secure distributed matrix multiplication"
    - "the capacity of robust private information retrieval with colluding databases"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "cross subspace alignment codes for coded distributed batch computation"):
  with members:
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "coded matrix multiplication on a group based model"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"
    - "hierarchical coding for distributed computing"

Connected through hyperedge HE-4 (induced by source paper "private and secure distributed matrix multiplication with flexible communication load"):
  with members:
    - "numerically stable polynomially coded computing"
    - "private sequential function computation"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "distributed and private coded matrix computation with flexible communication load"
    - "private information retrieval with private coded side information the multi server case"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "secure coded multi party computation for massive matrix operations"
    - "private secure coded computation"

Connected through hyperedge HE-5 (induced by source paper "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "private secure coded computation"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "private information retrieval from coded storage systems with colluding byzantine and unresponsive servers"
    - "gasp codes for secure distributed matrix multiplication"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"
    - "on the capacity of secure distributed matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "uplink downlink tradeoff in secure distributed matrix multiplication"):
  with members:
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "gasp codes for secure distributed matrix multiplication"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"
    - "on the capacity of secure distributed matrix multiplication"

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)