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: "how to incentivize data driven collaboration among competing parties"
  Abstract: The availability of vast amounts of data is changing how we can make medical discoveries, predict global market trends, save energy, and develop educational strategies. In some settings such as Genome Wide Association Studies or deep learning, sheer size of data seems critical. When data is held distributedly by many parties, they must share it to reap its full benefits. #R##N#One obstacle to this revolution is the lack of willingness of different parties to share data, due to reasons such as loss of privacy or competitive edge. Cryptographic works address privacy aspects, but shed no light on individual parties' losses/gains when access to data carries tangible rewards. Even if it is clear that better overall conclusions can be drawn from collaboration, are individual collaborators better off by collaborating? Addressing this question is the topic of this paper. #R##N#* We formalize a model of n-party collaboration for computing functions over private inputs in which participants receive their outputs in sequence, and the order depends on their private inputs. Each output "improves" on preceding outputs according to a score function. #R##N#* We say a mechanism for collaboration achieves collaborative equilibrium if it ensures higher reward for all participants when collaborating (rather than working alone). We show that in general, computing a collaborative equilibrium is NP-complete, yet we design efficient algorithms to compute it in a range of natural model settings. #R##N#Our collaboration mechanisms are in the standard model, and thus require a central trusted party; however, we show this assumption is unnecessary under standard cryptographic assumptions. We show how to implement the mechanisms in a decentralized way with new extensions of secure multiparty computation that impose order/timing constraints on output delivery to different players, as well as privacy and correctness.

Connected through hyperedge HE-1 (induced by source paper "raziel private and verifiable smart contracts on blockchains"):
  with members:
    - "smotherspectre exploiting speculative execution through port contention"
    - "a systematic evaluation of transient execution attacks and defenses"
    - "proposal for a fully decentralized blockchain and proof of work algorithm for solving np complete problems"
    - "cachezoom how sgx amplifies the power of cache attacks"
    - "chainspace a sharded smart contracts platform"
    - "an empirical analysis of smart contracts platforms applications and design patterns"
    - "software grand exposure sgx cache attacks are practical"
    - "rollback and forking detection for trusted execution environments using lightweight collective memory"

Connected through hyperedge HE-2 (induced by source paper "sharing information with competitors"):
  with members:
    - "a new approach to practical active secure two party computation"
    - "privacy aware mechanism design"
    - "rational secret sharing and multiparty computation extended abstract"

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.GT(Computer Science and Game Theory)