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: "robustness of neural networks against storage media errors"
  Abstract: We study the trade-offs between storage/bandwidth and prediction accuracy of neural networks that are stored in noisy media. Conventionally, it is assumed that all parameters (e.g., weight and biases) of a trained neural network are stored as binary arrays and are error-free. This assumption is based upon the implementation of error correction codes (ECCs) that correct potential bit flips in storage media. However, ECCs add storage overhead and cause bandwidth reduction when loading the trained parameters during the inference. We study the robustness of deep neural networks when bit errors exist but ECCs are turned off for different neural network models and datasets. It is observed that more sophisticated models and datasets are more vulnerable to errors in their trained parameters. We propose a simple detection approach that can universally improve the robustness, which in some cases can be improved by orders of magnitude. We also propose an alternative binary representation of the parameters such that the distortion brought by bit flips is reduced and even theoretically vanishing when the number of bits to represent a parameter increases.

Connected through hyperedge HE-1 (induced by source paper "functional error correction for robust neural networks"):
  with members:
    - "bit flip attack crushing neural network with progressive bit search"
    - "thinet a filter level pruning method for deep neural network compression"
    - "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 "eden enabling energy efficient high performance deep neural network inference using approximate dram"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "in dram bulk bitwise execution engine"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "nullanet training deep neural networks for reduced memory access inference"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-3 (induced by source paper "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"):
  with members:
    - "page cache attacks"
    - "scalable methods for 8 bit training of neural networks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sever a robust meta algorithm for stochastic optimization"
    - "breaking transferability of adversarial samples with randomness"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "training deep neural networks with 8 bit floating point numbers"
    - "nethammer inducing rowhammer faults through network requests"

Connected through hyperedge HE-4 (induced by source paper "machine learning for error correction with natural redundancy"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "end to end learning of communications systems without a channel model"
    - "learning convolutional networks for content weighted image compression"
    - "semantic perceptual image compression using deep convolution networks"
    - "fully convolutional networks for semantic segmentation"
    - "a new approach to content based file type detection"

Connected through hyperedge HE-5 (induced by source paper "in place zero space memory protection for cnn"):
  with members:
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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.IT(Information Theory)