human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "defensive collaborative multi task training defending against adversarial attack towards deep neural networks"
  Abstract: Deep neural network (DNNs) has shown impressive performance on hard perceptual problems. However, researchers found that DNN-based systems are vulnerable to adversarial examples which contain specially crafted humans-imperceptible perturbations. Such perturbations cause DNN-based systems to misclassify the adversarial examples, with potentially disastrous consequences where safety or security is crucial. As a major security concern, state-of-the-art attacks can still bypass the existing defensive methods. In this paper, we propose a novel defensive framework based on collaborative multi-task training to address the above problem. The proposed defence first incorporates specific label pairs into adversarial training process to enhance model robustness in black-box setting. Then a novel collaborative multi-task training framework is proposed to construct a detector which identifies adversarial examples based on the pairwise relationship of the label pairs. The detector can identify and reject high confidence adversarial examples that bypass black-box defence. The model whose robustness has been enhanced work reciprocally with the detector on the false-negative adversarial examples. Importantly, the proposed collaborative architecture can prevent the adversary from finding valid adversarial examples in a nearly-white-box setting.

Connected through hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

Question: Please tell me which class the center node 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)