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: "trusted multi party computation and verifiable simulations a scalable blockchain approach"
  Abstract: Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria framework is a computationally-intensive simulation used by various non-governmental and governmental agencies to understand malarial disease spread and effectiveness of intervention strategies, and subsequently design healthcare policies. Given that such shared results form the basis of inferences drawn, technological solutions designed, and day-to-day policies drafted, it is essential that the computations are validated and trusted. In particular, in a multi-agent environment involving several independent computing agents, a notion of trust in results generated by peers is critical in facilitating transparency, accountability, and collaboration. Using a novel combination of distributed validation of atomic computation blocks and a blockchain-based immutable audits mechanism, this work proposes a universal framework for distributed trust in computations. In particular we address the scalaibility problem by reducing the storage and communication costs using a lossy compression scheme. This framework guarantees not only verifiability of final results, but also the validity of local computations, and its cost-benefit tradeoffs are studied using a synthetic example of training a neural network.

Connected through hyperedge HE-1 (induced by source paper "parblockchain leveraging transaction parallelism in permissioned blockchain systems"):
  with members:
    - "fastfabric scaling hyperledger fabric to 20 000 transactions per second"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "performance benchmarking and optimizing hyperledger fabric blockchain platform"
    - "blockchains consensus protocols in the wild"
    - "a byzantine fault tolerant ordering service for the hyperledger fabric blockchain platform"
    - "adding concurrency to smart contracts"
    - "dgcc a new dependency graph based concurrency control protocol for multicore database systems"
    - "rethinking serializable multiversion concurrency control"

Connected through hyperedge HE-2 (induced by source paper "fastfabric scaling hyperledger fabric to 20 000 transactions per second"):
  with members:
    - "hotstuff bft consensus in the lens of blockchain"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "how to databasify a blockchain the case of hyperledger fabric"
    - "rcanopus making canopus resilient to failures and byzantine faults"
    - "consensus in the age of blockchains"
    - "a byzantine fault tolerant ordering service for the hyperledger fabric blockchain platform"
    - "blockbench a framework for analyzing private blockchains"

Connected through hyperedge HE-3 (induced by source paper "promoting distributed trust in machine learning and computational simulation via a blockchain network"):
  with members:
    - "algorithms that remember model inversion attacks and data protection law"
    - "open algorithms for identity federation"
    - "novel exploration techniques nets for malaria policy interventions"
    - "on the safety of machine learning cyber physical systems decision sciences and data products"

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.DC(Distributed, Parallel, and Cluster Computing)