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 "maskconnect connectivity learning by gradient descent"):
  Abstract: Although deep networks have recently emerged as the model of choice for many computer vision problems, in order to yield good results they often require time-consuming architecture search. To combat the complexity of design choices, prior work has adopted the principle of modularized design which consists in defining the network in terms of a composition of topologically identical or similar building blocks (a.k.a. modules). This reduces architecture search to the problem of determining the number of modules to compose and how to connect such modules. Again, for reasons of design complexity and training cost, previous approaches have relied on simple rules of connectivity, e.g., connecting each module to only the immediately preceding module or perhaps to all of the previous ones. Such simple connectivity rules are unlikely to yield the optimal architecture for the given problem.

Includes members:
  Member "multi fiber networks for video recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  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 "large scale evolution of image classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "blockdrop dynamic inference paths in residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "switchable precision neural networks")
  Member "hierarchical representations for efficient architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neuroevolution of self interpretable agents")
  Member "pathnet evolution channels gradient descent in super neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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.CV(Computer Vision and Pattern Recognition)