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 "branch and bound for piecewise linear neural network verification"):
  Abstract: The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. In this context, verification means verifying whether a NN model satisfies certain input-output properties. Despite the reputation of learned NN models as black boxes, and the theoretical hardness of proving useful properties about them, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. However, these methods are still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we make two key contributions. First, we present a unified framework based on branch and bound that encompasses previous methods. This analysis results in the identification of new methods that combine the strengths of multiple existing approaches, accomplishing a speedup of two orders of magnitude compared to the previous state of the art. Second, we propose a new data set of benchmarks which includes a collection of previously released test cases. We use the benchmark to provide a thorough experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems.

Includes members:
  Member "neural network branching for neural network verification":
    also connected through other hyperedges:
      (none)
  Member "fast neural network verification via shadow prices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "verification of neural network control policy under persistent adversarial perturbation")
  Member "formal security analysis of neural networks using symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robustness verification for transformers")
  Member "a dual approach to scalable verification of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness verification for transformers")
  Member "efficient formal safety analysis of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "an approach to reachability analysis for feed forward relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "output reachable set estimation and verification for multi layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "testing deep neural networks")
  Member "formal guarantees on the robustness of a classifier against adversarial manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robustness verification for transformers")

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)