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 "relevant features based auxiliary cells for energy efficient detection of natural errors"):
  Abstract: Deep neural networks have demonstrated state-of-the-art performance on many classification tasks. However, they have no inherent capability to recognize when their predictions are wrong. There have been several efforts in the recent past to detect natural errors but the suggested mechanisms pose additional energy requirements. To address this issue, we propose an ensemble of classifiers at hidden layers to enable energy efficient detection of natural errors. In particular, we append Relevant-features based Auxiliary Cells (RACs) which are class specific binary linear classifiers trained on relevant features. The consensus of RACs is used to detect natural errors. Based on combined confidence of RACs, classification can be terminated early, thereby resulting in energy efficient detection. We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR-10, CIFAR-100 and Tiny-ImageNet.

Includes members:
  Member "natural and adversarial error detection using invariance to image transformations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial example detection and classification with asymmetrical adversarial training")
  Member "designing adaptive neural networks for energy constrained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "distributed deep neural networks over the cloud the edge and end devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "run time deep model multiplexing")
  Member "distance based confidence score for neural network classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "controlling risk of web question answering")
  Member "a baseline for detecting misclassified and out of distribution examples in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "conditional deep learning for energy efficient and enhanced pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic convolutions exploiting spatial sparsity for faster inference")

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)