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: "ontology based secure retrieval of semantically significant visual contents"
  Abstract: Image classification is an enthusiastic research field where large amount of image data is classified into various classes based on their visual contents. Researchers have presented various low-level features-based techniques for classifying images into different categories. However, efficient and effective classification and retrieval is still a challenging problem due to complex nature of visual contents. In addition, the traditional information retrieval techniques are vulnerable to security risks, making it easy for attackers to retrieve personal visual contents such as patients records and law enforcement agencies databases. Therefore, we propose a novel ontology-based framework using image steganography for secure image classification and information retrieval. The proposed framework uses domain-specific ontology for mapping the low-level image features to high-level concepts of ontologies which consequently results in efficient classification. Furthermore, the proposed method utilizes image steganography for hiding the image semantics as a secret message inside them, making the information retrieval process secure from third parties. The proposed framework minimizes the computational complexity of traditional techniques, increasing its suitability for secure and real-time visual contents retrieval from personalized image databases. Experimental results confirm the efficiency, effectiveness, and security of the proposed framework as compared with other state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "a new image steganographic technique using pattern based bits shuffling and magic lsb for grayscale images"):
  with members:
    - "a novel magic lsb substitution method m lsb sm using multi level encryption and achromatic component of an image"
    - "an adaptive secret key directed cryptographic scheme for secure transmission in wireless sensor networks"
    - "describing colors textures and shapes for content based image retrieval a survey"
    - "a secure cyclic steganographic technique for color images using randomization"
    - "a novel image steganographic approach for hiding text in color images using hsi color model"
    - "secure image steganography using cryptography and image transposition"

Connected through hyperedge HE-2 (induced by source paper "steganography a secure way for transmission in wireless sensor networks"):
  with members:
    - "a novel magic lsb substitution method m lsb sm using multi level encryption and achromatic component of an image"
    - "a new image steganographic technique using pattern based bits shuffling and magic lsb for grayscale images"
    - "an adaptive secret key directed cryptographic scheme for secure transmission in wireless sensor networks"
    - "describing colors textures and shapes for content based image retrieval a survey"
    - "a secure cyclic steganographic technique for color images using randomization"
    - "a novel image steganographic approach for hiding text in color images using hsi color model"
    - "secure image steganography using cryptography and image transposition"
    - "on topological properties of wireless sensor networks under the q composite key predistribution scheme with on off channels"

Connected through hyperedge HE-3 (induced by source paper "an adaptive secret key directed cryptographic scheme for secure transmission in wireless sensor networks"):
  with members:
    - "a secure cyclic steganographic technique for color images using randomization"
    - "a novel image steganographic approach for hiding text in color images using hsi color model"
    - "on topological properties of wireless sensor networks under the q composite key predistribution scheme with on off channels"
    - "security in wireless sensor networks issues and challenges"

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.MM(Multimedia)