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: "aloha auxiliary loss optimization for hypothesis augmentation"
  Abstract: Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely match the samples' true malicious/benign (1/0) target labels. However, there are often a number of other sources of contextual metadata for each malware sample, beyond an aggregate malicious/benign label, including multiple labeling sources and malware type information (e.g., ransomware, trojan, etc.), which we can feed to the classifier as auxiliary prediction targets. In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss. We find that incorporating multiple auxiliary loss terms yields a marked improvement in performance on the main detection task. We also demonstrate that these gains likely stem from a more informed neural network representation and are not due to a regularization artifact of multi-target learning. Our auxiliary loss architecture yields a significant reduction in detection error rate (false negatives) of 42.6% at a false positive rate (FPR) of $10^{-3}$ when compared to a similar model with only one target, and a decrease of 53.8% at $10^{-5}$ FPR.

Connected through hyperedge HE-1 (induced by source paper "automatic malware description via attribute tagging and similarity embedding"):
  with members:
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "fast zero shot image tagging"
    - "are facial attributes adversarially robust"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "microsoft malware classification challenge"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"

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.CR(Cryptography and Security)