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 "distilled hierarchical neural ensembles with adaptive inference cost"):
  Abstract: Deep neural networks form the basis of state-of-the-art models across a variety of application domains. Moreover, networks that are able to dynamically adapt the computational cost of inference are important in scenarios where the amount of compute or input data varies over time. In this paper, we propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks by sharing intermediate layers using a hierarchical structure. In HNE we control the inference cost by evaluating only a subset of models, which are organized in a nested manner. Our second contribution is a novel co-distillation method to boost the performance of ensemble predictions with low inference cost. This approach leverages the nested structure of our ensembles, to optimally allocate accuracy and diversity across the ensemble members. Comprehensive experiments over the CIFAR and Ima-geNet datasets confirm the effectiveness of HNE in building deep networks with adaptive inference cost for image classification.

Includes members:
  Member "adaptative inference cost with convolutional neural mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "switchable precision neural networks")
  Member "depth adaptive transformer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "depth adaptive graph recurrent network for text classification")
  Member "hydra an ensemble of convolutional neural networks for geospatial land classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coopnet cooperative convolutional neural network for low power mcus")
  Member "robust training with ensemble consensus":
    also connected through other hyperedges:
      (none)
  Member "deep tree learning for zero shot face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching central difference convolutional networks for face anti spoofing")
  Member "improved techniques for training adaptive deep networks":
    also connected through other hyperedges:
      (none)
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "universally slimmable networks and improved training techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "switchable precision neural networks")

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)