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: "zebralancer private and anonymous crowdsourcing system atop open blockchain"
  Abstract: We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e. data leakage and identity breach. First, our outsource-then-prove methodology resolves the tension between blockchain transparency and data confidentiality, which is critical in crowdsourcing use-case. ZebraLancer ensures: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Furthermore, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. On the other hand, allowing anonymity will enable a malicious worker to submit multiple times to reap rewards. ZebraLancer overcomes this problem by allowing anonymous requests/submissions without sacrificing the accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e. the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol with the existing real-world blockchain.

Connected through hyperedge HE-1 (induced by source paper "a blockchain based approach for saving and tracking differential privacy cost"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "collecting telemetry data privately"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "generalization in adaptive data analysis and holdout reuse"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-2 (induced by source paper "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain"):
  with members:
    - "access control for electronic health records with hybrid blockchain edge architecture"
    - "detecting sybil attacks using proofs of work and location in vanets"
    - "privacy preserving smart parking system using blockchain and private information retrieval"
    - "blockchain based firmware update scheme tailored for autonomous vehicles"
    - "blockchain based charging coordination mechanism for smart grid energy storage units"
    - "blockchain inspired event recording system for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "onionchain towards balancing privacy and traceability of blockchain based applications"):
  with members:
    - "an empirical analysis of linkability in the monero blockchain"
    - "intelligent vehicle trust point reward based intelligent vehicle communication using blockchain"
    - "blockchain a distributed solution to automotive security and privacy"
    - "blockchain based intelligent vehicle data sharing framework"
    - "securing vehicle to vehicle communications using blockchain through visible light and acoustic side channels"

Connected through hyperedge HE-4 (induced by source paper "infochain a decentralized system for truthful information elicitation"):
  with members:
    - "securely trading unverifiable information without trust"
    - "informed truthfulness in multi task peer prediction"
    - "crowdsourced judgement elicitation with endogenous proficiency"

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.HC(Human-Computer Interaction)