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: "imbalanced malware images classification a cnn based approach"
  Abstract: Deep convolutional neural networks (CNNs) can be applied to malware binary detection through images classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs. The original softmax loss is weighted, and the weight value can be determined according to class size. A scaling parameter is also included in computing the weight. Proper selection of this parameter has been studied and an empirical option is given. The weighted loss aims at alleviating the impact of data imbalance in an end-to-end learning fashion. To validate the efficacy, we deploy the proposed weighted loss in a pre-trained deep CNN model and fine-tune it to achieve promising results on malware images classification. Extensive experiments also indicate that the new loss function can fit other typical CNNs with an improved classification performance.

Connected through hyperedge HE-1 (induced by source paper "generation evaluation of adversarial examples for malware obfuscation"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "microsoft malware classification challenge"
    - "black box attacks against rnn based malware detection algorithms"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "xgboost a scalable tree boosting system"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "random capsnet forest model for imbalanced malware type classification task"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "capsacc an efficient hardware accelerator for capsulenets with data reuse"
    - "fast capsnet for lung cancer screening"
    - "deep transfer learning for static malware classification"
    - "brain tumor type classification via capsule networks"
    - "microsoft malware classification challenge"
    - "deep learning at the shallow end malware classification for non domain experts"

Connected through hyperedge HE-3 (induced by source paper "weighted point cloud augmentation for neural network training data class imbalance"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "splatnet sparse lattice networks for point cloud processing"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "focal loss for dense object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "to believe or not to believe validating explanation fidelity for dynamic malware analysis"):
  with members:
    - "deep transfer learning for static malware classification"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "a unified approach to interpreting model predictions"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"):
  with members:
    - "towards resilient machine learning for ransomware detection"
    - "deep transfer learning for static malware classification"
    - "microsoft malware classification challenge"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "deep transfer learning for static malware classification"):
  with members:
    - "microsoft malware classification challenge"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "lightweight classification of iot malware based on image recognition"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "xgboost a scalable tree boosting system"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "microsoft malware classification challenge"):
  with members:
    - "learning a neural network based representation for open set recognition"
    - "lempel ziv jaccard distance an effective alternative to ssdeep and sdhash"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "itect scalable information theoretic similarity for malware detection"
    - "on the feasibility of malware authorship attribution"
    - "building better detection with privileged information"
    - "a theoretical framework for robustness of deep classifiers against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "lightweight classification of iot malware based on image recognition"):
  with members:
    - "dropneuron simplifying the structure of deep neural networks"
    - "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.CV(Computer Vision and Pattern Recognition)