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: "stochastic optimization for machine learning"
  Abstract: It has been found that stochastic algorithms often find good solutions much more rapidly than inherently-batch approaches. Indeed, a very useful rule of thumb is that often, when solving a machine learning problem, an iterative technique which relies on performing a very large number of relatively-inexpensive updates will often outperform one which performs a smaller number of much "smarter" but computationally-expensive updates. #R##N#In this thesis, we will consider the application of stochastic algorithms to two of the most important machine learning problems. Part i is concerned with the supervised problem of binary classification using kernelized linear classifiers, for which the data have labels belonging to exactly two classes (e.g. "has cancer" or "doesn't have cancer"), and the learning problem is to find a linear classifier which is best at predicting the label. In Part ii, we will consider the unsupervised problem of Principal Component Analysis, for which the learning task is to find the directions which contain most of the variance of the data distribution. #R##N#Our goal is to present stochastic algorithms for both problems which are, above all, practical--they work well on real-world data, in some cases better than all known competing algorithms. A secondary, but still very important, goal is to derive theoretical bounds on the performance of these algorithms which are at least competitive with, and often better than, those known for other approaches.

Connected through hyperedge HE-1 (induced by source paper "the wireless control plane an overview and directions for future research"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a statistical characterization of localization performance in wireless networks"
    - "deep reinforcement learning a brief survey"
    - "survey of important issues in uav communication networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "achieving ultra low latency in 5g millimeter wave cellular networks"
    - "small cell offloading through cooperative communication in software defined heterogeneous networks"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "stochastic proximal auc maximization"):
  with members:
    - "introduction to online convex optimization"
    - "stochastic variance reduction methods for saddle point problems"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "one pass auc optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-3 (induced by source paper "a univariate bound of area under roc"):
  with members:
    - "on the consistency of auc pairwise optimization"

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)