human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning with non convex truncated losses by sgd"):
  Abstract: Learning with a {\it convex loss} function has been a dominating paradigm for many years. It remains an interesting question how non-convex loss functions help improve the generalization of learning with broad applicability. In this paper, we study a family of objective functions formed by truncating traditional loss functions, which is applicable to both shallow learning and deep learning. Truncating loss functions has potential to be less vulnerable and more robust to large noise in observations that could be adversarial. More importantly, it is a generic technique without assuming the knowledge of noise distribution. To justify non-convex learning with truncated losses, we establish excess risk bounds of empirical risk minimization based on truncated losses for heavy-tailed output, and statistical error of an approximate stationary point found by stochastic gradient descent (SGD) method. Our experiments for shallow and deep learning for regression with outliers, corrupted data and heavy-tailed noise further justify the proposed method.

Includes members:
  Member "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs")
  Member "fast rates for general unbounded loss functions from erm to generalized bayes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pac bayes un expected bernstein inequality")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")
  Member "from stochastic mixability to fast rates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pac bayes un expected bernstein inequality")
  Member "relative deviation learning bounds and generalization with unbounded loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gradient aware model based policy search")
  Member "loss minimization and parameter estimation with heavy tails":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tadam a robust stochastic gradient optimizer")

Question: Please tell me which class the source paper 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)