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 upload versus download cost for secure and private matrix multiplication"
  Abstract: In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix $A$, with a matrix $B_\theta$, where $\theta\in\{1,\dots,M\}$. The set of candidate matrices $\{B_1,\dots,B_M\}$ are public, and available at all the $N$ servers. The goal of the user is to distributedly compute $AB_{\theta}$, such that $(a)$ no information is leaked about the matrix $A$ to any server; and $(b)$ the index $\theta$ is kept private from each server. Our goal is to understand the fundamental tradeoff between the upload vs download cost for this problem. Our main contribution is to show that the lower convex hull of following (upload, download) pairs: $(U,D)=(N/(K-1),(K/(K-1))(1+(K/N)+\dots+(K/N)^{M-1}))$ for $K=2,\dots,N$ is achievable. The scheme improves upon state-of-the-art existing schemes for this problem, and leverages ideas from secret sharing and coded private information retrieval.

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"
    - "on the capacity of secure distributed matrix multiplication"
    - "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"

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 capacity of secure distributed 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 "private sequential function computation"):
  with members:
    - "private inner product retrieval for distributed machine learning"
    - "on the capacity of computation broadcast"
    - "private information retrieval with private coded side information the multi server case"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "private polynomial computation for noncolluding coded databases"
    - "private proximity retrieval codes"
    - "achievable rate of private function retrieval from mds coded databases"
    - "robust private information retrieval from coded systems with byzantine and colluding servers"

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"
    - "on the capacity of secure distributed matrix multiplication"

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"
    - "on the capacity of secure distributed matrix multiplication"
    - "private secure coded computation"
    - "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 capacity of secure distributed 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"

Connected through hyperedge HE-7 (induced by source paper "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"):
  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"
    - "uplink downlink tradeoff in secure distributed matrix multiplication"
    - "polyshard coded sharding achieves linearly scaling efficiency and security simultaneously"
    - "speeding up distributed machine learning using codes"

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)