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: "polisis automated analysis and presentation of privacy policies using deep learning"
  Abstract: Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. In their current form, policies remain long and difficult to comprehend, thus merely serving the goal of legally protecting the companies. Short notices based on information extracted from privacy policies have been shown to be useful and more usable, but face a significant scalability hurdle, given the number of policies and their evolution over time. Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies. To address these hurdles, we propose Polisis, an automated framework for privacy Policies analysis. It enables scalable, dynamic, and multi-dimensional queries on privacy policies. At the core of Polisis is a privacy-centric language model, built with 130K privacy policies, and a novel hierarchy of neural network classifiers that caters to the high-level aspects and the fine-grained details of privacy practices. We demonstrate Polisis's modularity and utility with two robust applications that support structured and free-form querying. The structured querying application is the automated assignment of privacy icons from the privacy policies. With Polisis, we can achieve an accuracy of 88.4% on this task, when evaluated against earlier annotations by a group of three legal experts. The second application is PriBot, the first free-form Question Answering about Privacy policies. We show that PriBot can produce a correct answer among its top-3 results for 82% of the test questions.

Connected through hyperedge HE-1 (induced by source paper "quality assessment of online automated privacy policy generators an empirical study"):
  with members:
    - "an empirical assessment of security risks of global android banking apps"

Connected through hyperedge HE-2 (induced by source paper "question answering for privacy policies combining computational and legal perspectives"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "quac question answering in context"
    - "stress test evaluation for natural language inference"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "claudette an automated detector of potentially unfair clauses in online terms of service"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "we value your privacy now take some cookies measuring the gdpr s impact on web privacy"):
  with members:
    - "tranco a research oriented top sites ranking hardened against manipulation"
    - "an automated approach to auditing disclosure of third party data collection in website privacy policies"
    - "a long way to the top significance structure and stability of internet top lists"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-5 (induced by source paper "the privacy policy landscape after the gdpr"):
  with members:
    - "claudette an automated detector of potentially unfair clauses in online terms of service"
    - "automatic detection of vague words and sentences in privacy policies"
    - "we value your privacy now take some cookies measuring the gdpr s impact on web privacy"
    - "convolutional neural networks for sentence classification"

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.CL(Computation and Language)