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: "placeraider virtual theft in physical spaces with smartphones"
  Abstract: As smartphones become more pervasive, they are increasingly targeted by malware. At the same time, each new generation of smartphone features increasingly powerful onboard sensor suites. A new strain of sensor malware has been developing that leverages these sensors to steal information from the physical environment (e.g., researchers have recently demonstrated how malware can listen for spoken credit card numbers through the microphone, or feel keystroke vibrations using the accelerometer). Yet the possibilities of what malware can see through a camera have been understudied. This paper introduces a novel visual malware called PlaceRaider, which allows remote attackers to engage in remote reconnaissance and what we call virtual theft. Through completely opportunistic use of the camera on the phone and other sensors, PlaceRaider constructs rich, three dimensional models of indoor environments. Remote burglars can thus download the physical space, study the environment carefully, and steal virtual objects from the environment (such as financial documents, information on computer monitors, and personally identifiable information). Through two human subject studies we demonstrate the effectiveness of using mobile devices as powerful surveillance and virtual theft platforms, and we suggest several possible defenses against visual malware.

Connected through hyperedge HE-1 (induced by source paper "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"):
  with members:
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hidden two stream convolutional networks for action recognition"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "convolutional two stream network fusion for video action recognition"
    - "studying very low resolution recognition using deep networks"
    - "deep temporal linear encoding networks"

Connected through hyperedge HE-2 (induced by source paper "a secure mobile authentication alternative to biometrics"):
  with members:
    - "video liveness for citizen journalism attacks and defenses"
    - "camera based two factor authentication through mobile and wearable devices"
    - "yfcc100m the new data in multimedia research"
    - "tensorflow a system for large scale machine learning"
    - "armageddon cache attacks on mobile devices"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "sound proof usable two factor authentication based on ambient sound"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-3 (induced by source paper "deeprotect enabling inference based access control on mobile sensing applications"):
  with members:
    - "6thsense a context aware sensor based attack detector for smart devices"
    - "multi sensor authentication to improve smartphone security"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "caffe convolutional architecture for fast feature embedding"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"
    - "participatory privacy enabling privacy in participatory sensing"
    - "blind compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "a study on power side channels on mobile devices"):
  with members:
    - "powerspy location tracking using mobile device power analysis"

Connected through hyperedge HE-5 (induced by source paper "screenavoider protecting computer screens from ubiquitous cameras"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"

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)