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: "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
  Abstract: With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.

Connected through hyperedge HE-1 (induced by source paper "a convolutional neural network for user identification based on motion sensors"):
  with members:
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "attention is all you need"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "learning human identity from motion patterns"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-2 (induced by source paper "deep learning towards mobile applications"):
  with members:
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning differentially private recurrent language models"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-3 (induced by source paper "not just privacy improving performance of private deep learning in mobile cloud"):
  with members:
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "adversarial dropout for supervised and semi supervised learning"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"

Connected through hyperedge HE-4 (induced by source paper "adversarial attack and defense on graph data a survey"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "exploring the space of black box attacks on deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data poisoning attack against unsupervised node embedding methods"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "joint embedding of meta path and meta graph for heterogeneous information networks"):
  with members:
    - "multi round influence maximization"
    - "contaminant removal for android malware detection systems"
    - "online unsupervised multi view feature selection"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "layerwise perturbation based adversarial training for hard drive health degree prediction"):
  with members:
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "regularizing deep networks using efficient layerwise adversarial training"
    - "adversarial machine learning at scale"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "speech recognition with deep recurrent neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "a self organizing tensor architecture for multi view clustering"):
  with members:
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "a survey on multi view clustering"
    - "online unsupervised multi view feature selection"

Connected through hyperedge HE-8 (induced by source paper "contaminant removal for android malware detection systems"):
  with members:
    - "multi view unsupervised feature selection by cross diffused matrix alignment"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"

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)