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 "the information complexity of learning tasks their structure and their distance"):
  Abstract: We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational to the practice of transfer learning, ubiquitous in Deep Learning, whereby a parametric model is pre-trained for a task, and then used for another after fine-tuning. The framework we develop is intrinsically non-asymptotic, capturing the finite nature of the training dataset, yet it allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity, Shannon, and Fisher Information. However, unlike some of those frameworks, it can be applied easily to large-scale models and real-world datasets. It is the first framework to explicitly account for the optimization scheme, which plays a crucial role in Deep Learning, in measuring complexity and information.

Includes members:
  Member "the dynamics of differential learning i information dynamics and task reachability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "domain adaptation for visual applications a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "emergence of invariance and disentanglement in deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "critical learning periods in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the break even point on optimization trajectories of deep neural networks")
  Member "new insights and perspectives on the natural gradient method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise")
  Member "a pac bayesian tutorial with a dropout bound":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)