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: "privacy in the genomic era"
  Abstract: Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.

Connected through hyperedge HE-1 (induced by source paper "genome reconstruction attacks against genomic data sharing beacons"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "the chills and thrills of whole genome sequencing"
    - "identifying participants in the personal genome project by name a re identification experiment"

Connected through hyperedge HE-2 (induced by source paper "effcient logging and querying for blockchain based cross site genomic dataset access audit"):
  with members:
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "modelchain decentralized privacy preserving healthcare predictive modeling framework on private blockchain networks"
    - "blockbench a framework for analyzing private blockchains"
    - "untangling blockchain a data processing view of blockchain systems"
    - "optimized execution of business processes on blockchain"
    - "enigma decentralized computation platform with guaranteed privacy"

Connected through hyperedge HE-3 (induced by source paper "evaluating the strength of genomic privacy metrics"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "the mastermind attack on genomic data"
    - "de anonymizing social networks"

Connected through hyperedge HE-4 (induced by source paper "collusion secure watermarking for sequential data"):
  with members:
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-5 (induced by source paper "patient driven privacy control through generalized distillation"):
  with members:
    - "feature selection a data perspective"
    - "curie policy based secure data exchange"
    - "building better detection with privileged information"
    - "extending detection with forensic information"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "adam a method for stochastic optimization"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-6 (induced by source paper "lopub high dimensional crowdsourced data publication with local differential privacy"):
  with members:
    - "optimal differentially private mechanisms for randomised response"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

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)