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 "modular universal reparameterization deep multi task learning across diverse domains"):
  Abstract: As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? To approach this question, deep multi-task learning is extended in this paper to the setting where there is no obvious overlap between task architectures. The idea is that any set of (architecture,task) pairs can be decomposed into a set of potentially related subproblems, whose sharing is optimized by an efficient stochastic algorithm. The approach is first validated in a classic synthetic multi-task learning benchmark, and then applied to sharing across disparate architectures for vision, NLP, and genomics tasks. It discovers regularities across these domains, encodes them into sharable modules, and combines these modules systematically to improve performance in the individual tasks. The results confirm that sharing learned functionality across diverse domains and architectures is indeed beneficial, thus establishing a key ingredient for general problem solving in the future.

Includes members:
  Member "evolutionary architecture search for deep multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deeper insights into weight sharing in neural architecture search")
  Member "pseudo task augmentation from deep multitask learning to intratask sharing and back":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "frosting weights for better continual training")
  Member "outrageously large neural networks the sparsely gated mixture of experts layer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention over parameters for dialogue systems")
  Member "routing networks adaptive selection of non linear functions for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention over parameters for dialogue systems")
  Member "learning multiple visual domains with residual adapters":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "regularizing and optimizing lstm language models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "distral robust multitask reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")

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)