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 "characterizing the shape of activation space in deep neural networks"):
  Abstract: The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure. We introduce a method for computing persistent homology over the graphical activation structure of neural networks, which provides access to the task-relevant substructures activated throughout the network for a given input. This topological perspective provides unique insights into the distributed representations encoded by neural networks in terms of the shape of their activation structures. We demonstrate the value of this approach by showing an alternative explanation for the existence of adversarial examples. By studying the topology of network activations across multiple architectures and datasets, we find that adversarial perturbations do not add activations that target the semantic structure of the adversarial class as previously hypothesized. Rather, adversarial examples are explainable as alterations to the dominant activation structures induced by the original image, suggesting the class representations learned by deep networks are problematically sparse on the input space.

Includes members:
  Member "topology of learning in artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "classifying the classifier dissecting the weight space of neural networks")
  Member "neural persistence a complexity measure for deep neural networks using algebraic topology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topologically densified distributions")
  Member "adversarial examples a complete characterisation of the phenomenon":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "impact of low bitwidth quantization on the adversarial robustness for embedded neural networks")
  Member "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "on characterizing the capacity of neural networks using algebraic topology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geometry and topology of deep neural networks decision boundaries")
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "detecting adversarial perturbations through spatial behavior in activation spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when explainability meets adversarial learning detecting adversarial examples using shap signatures")

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)