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: "achieving efficient and secure data acquisition for cloud supported internet of things in smart grid"
  Abstract: Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on CP-ABE (Ciphertext Policy Attribute Based Encryption). Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access sub-tree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-supported IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.

Connected through hyperedge HE-1 (induced by source paper "interest related item similarity model based on multimodal data for top n recommendation"):
  with members:
    - "adversarial training towards robust multimedia recommender system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aesthetic based clothing recommendation"
    - "nais neural attentive item similarity model for recommendation"
    - "a multimodal recommender system for large scale assortment generation in e commerce"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "achieving data utility privacy tradeoff in internet of medical things a machine learning approach"):
  with members:
    - "privacy preserving and efficient aggregation based on blockchain for power grid communications in smart communities"
    - "differentially private k means clustering"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-3 (induced by source paper "an accountable anonymous data aggregation scheme for internet of things"):
  with members:
    - "riposte an anonymous messaging system handling millions of users"
    - "proactively accountable anonymous messaging in verdict"

Connected through hyperedge HE-4 (induced by source paper "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"):
  with members:
    - "differential privacy as a mutual information constraint"

Connected through hyperedge HE-5 (induced by source paper "active learning for wireless iot intrusion detection"):
  with members:
    - "xgboost a scalable tree boosting system"

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)