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: "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"
  Abstract: Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid for their work. Contracts can be created easily by anyone with a dataset, even programmatically by software agents. This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents.

Connected through hyperedge HE-1 (induced by source paper "trustable and automated machine learning running with blockchain and its applications"):
  with members:
    - "a unified analytical framework for trustable machine learning and automation running with blockchain"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dynamic routing between capsules"
    - "toward geometric deep slam"
    - "xgboost a scalable tree boosting system"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving blockchain mining sybil resistance by proof of useful work"):
  with members:
    - "secure computation for machine learning with spdz"
    - "a scalable verification solution for blockchains"
    - "dfinity technology overview series consensus system"
    - "consensus in the age of blockchains"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "bitcoin ng a scalable blockchain protocol"
    - "enigma decentralized computation platform with guaranteed privacy"
    - "bitcoin meets strong consistency"

Connected through hyperedge HE-3 (induced by source paper "decentralized and collaborative ai on blockchain"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "universal sentence encoder"
    - "casper the friendly finality gadget"
    - "a collaborative mechanism for crowdsourcing prediction problems"

Connected through hyperedge HE-4 (induced by source paper "ownership preserving ai market places using blockchain"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"

Connected through hyperedge HE-5 (induced by source paper "a unified analytical framework for trustable machine learning and automation running with blockchain"):
  with members:
    - "distributed double spending prevention"

Connected through hyperedge HE-6 (induced by source paper "blockchain enabled trustless api marketplace"):
  with members:
    - "gazelle a low latency framework for secure neural network inference"

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)