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: "theoretical limits of one shot distributed learning"
  Abstract: We consider a distributed system of $m$ machines and a server. Each machine draws $n$ i.i.d samples from an unknown distribution and sends a message of bounded length $b$ to the server. The server then collects messages from all machines and estimates a parameter that minimizes an expected loss. We investigate the impact of communication constraint, $b$, on the expected error; and derive lower bounds on the best error achievable by any algorithm. As our main result, for general values of $b$, we establish a $\tilde{\Omega}\big( (mb)^{-{1}/{\max(d,2)}} n^{-1/2} \big)$ lower bounded on the expected error, where $d$ is the dimension of the parameter space. Moreover, for constant values of $b$ and under the extra assumption $n=1$, we show that expected error remains lower bounded by a constant, even when $m$ tends to infinity.

Connected through hyperedge HE-1 (induced by source paper "order optimal one shot distributed learning"):
  with members:
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "deep learning with elastic averaging sgd"

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.LG(Machine Learning)