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: "bit flip attack crushing neural network with progressive bit search"
  Abstract: Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated security concern of DNN is from its malicious input, a.k.a adversarial example. Nevertheless, the security challenge of DNN's parameters is not well explored yet. In this work, we are the first to propose a novel DNN weight attack methodology called Bit-Flip Attack (BFA) which can crush a neural network through maliciously flipping extremely small amount of bits within its weight storage memory system (i.e., DRAM). The bit-flip operations could be conducted through well-known Row-Hammer attack, while our main contribution is to develop an algorithm to identify the most vulnerable bits of DNN weight parameters (stored in memory as binary bits), that could maximize the accuracy degradation with a minimum number of bit-flips. Our proposed BFA utilizes a Progressive Bit Search (PBS) method which combines gradient ranking and progressive search to identify the most vulnerable bit to be flipped. With the aid of PBS, we can successfully attack a ResNet-18 fully malfunction (i.e., top-1 accuracy degrade from 69.8% to 0.1%) only through 13 bit-flips out of 93 million bits, while randomly flipping 100 bits merely degrades the accuracy by less than 1%.

Connected through hyperedge HE-1 (induced by source paper "functional error correction for robust neural networks"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "robustness of neural networks against storage media errors"
    - "overcoming catastrophic forgetting in neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "continuous control with deep reinforcement learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "survey of attacks and defenses on edge deployed neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "xonn xnor based oblivious deep neural network inference"
    - "deeplaser practical fault attack on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-3 (induced by source paper "tbt targeted neural network attack with bit trojan"):
  with members:
    - "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "hardware trojan attacks on neural networks"
    - "enhancing robustness of machine learning systems via data transformations"
    - "another flip in the wall of rowhammer defenses"

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.CV(Computer Vision and Pattern Recognition)