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: "private coded computation for machine learning"
  Abstract: In a distributed computing system for the master-worker framework, an erasure code can mitigate the effects of slow workers, also called stragglers. The distributed computing system combined with coding is referred to as coded computation. We introduce a variation of coded computation that protects the master's privacy from the workers, which is referred to as private coded computation. In private coded computation, the master needs to compute a function of its own dataset and one of the datasets in a library exclusively shared by the external workers. After the master recovers the result of the desired function through coded computation, the workers should not know which dataset in the library was desired by the master, which implies that the master's privacy is protected. We propose a private coded computation scheme for matrix multiplication, namely private polynomial codes, based on polynomial codes for conventional coded computation. As special cases of private polynomial codes, we propose private one-shot polynomial codes and private asynchronous polynomial codes. Whereas the private one-shot polynomial code achieves a lower communication load from the master to each worker, the private asynchronous polynomial code achieves faster computation than private one-shot polynomial codes. In terms of computation time and communication load, we compare private one-shot polynomial codes and private asynchronous polynomial codes with a conventional robust private information retrieval scheme which can be directly applied to coded computation.

Connected through hyperedge HE-1 (induced by source paper "private secure coded computation"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "on the capacity of secure distributed matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "private function retrieval"
    - "a fundamental tradeoff between computation and communication in distributed computing"

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)