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: "optimal robust learning of discrete distributions from batches"
  Abstract: Many applications, including natural language processing, sensor networks, collaborative filtering, and federated learning, call for estimating discrete distributions from data collected in batches, some of which may be untrustworthy, erroneous, faulty, or even adversarial. #R##N#Previous estimators for this setting ran in exponential time, and for some regimes required a suboptimal number of batches. We provide the first polynomial-time estimator that is optimal in the number of batches and achieves essentially the best possible estimation accuracy.

Connected through hyperedge HE-1 (induced by source paper "learning structured distributions from untrusted batches faster and simpler"):
  with members:
    - "recent advances in algorithmic high dimensional robust statistics"
    - "efficiently learning structured distributions from untrusted batches"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "federated learning strategies for improving communication efficiency"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "learning discrete distributions from untrusted batches"
    - "learning populations of parameters"

Connected through hyperedge HE-2 (induced by source paper "on the sample complexity of adversarial multi source pac learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "do outliers ruin collaboration"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "mitigating sybils in federated learning poisoning"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "on fundamental limits of robust learning"

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)