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: "a marketplace for data an algorithmic solution"
  Abstract: In this work, we aim to design a data marketplace; a robust real-time matching mechanism to efficiently buy and sell training data for Machine Learning tasks. While the monetization of data and pre-trained models is an essential focus of industry today, there does not exist a market mechanism to price training data and match buyers to sellers while still addressing the associated (computational and other) complexity. The challenge in creating such a market stems from the very nature of data as an asset: (i) it is freely replicable; (ii) its value is inherently combinatorial due to correlation with signal in other data; (iii) prediction tasks and the value of accuracy vary widely; (iv) usefulness of training data is difficult to verify a priori without first applying it to a prediction task. As our main contributions we: (i) propose a mathematical model for a two-sided data market and formally define the key associated challenges; (ii) construct algorithms for such a market to function and analyze how they meet the challenges defined. We highlight two technical contributions: (i) a new notion of 'fairness' required for cooperative games with freely replicable goods; (ii) a truthful, zero regret mechanism to auction a class of combinatorial goods based on utilizing Myerson's payment function and the Multiplicative Weights algorithm. These might be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "a distributional framework for data valuation"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "making ai forget you data deletion in machine learning"
    - "who s responsible jointly quantifying the contribution of the learning algorithm and training data"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "a unified approach to interpreting model predictions"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "data market platforms trading data assets to solve data problems vision paper"):
  with members:
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "revenue maximization for query pricing"
    - "truth finding on the deep web is the problem solved"
    - "a survey on truth discovery"
    - "a theory of pricing private data"

Connected through hyperedge HE-3 (induced by source paper "towards data auctions with externalities"):
  with members:
    - "low cost learning via active data procurement"
    - "budget feasible mechanisms for experimental design"
    - "conducting truthful surveys cheaply"
    - "selling privacy at auction"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "computing the value of spatio temporal data in wholesale and retail data marketplaces"):
  with members:
    - "valuating user data in a human centric data economy"
    - "computing shapley values in the plane"
    - "optimal mechanisms for selling information"
    - "the cost of stability in coalitional games"

Connected through hyperedge HE-5 (induced by source paper "who s responsible jointly quantifying the contribution of the learning algorithm and training data"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "multiaccuracy black box post processing for fairness in classification"
    - "consistent individualized feature attribution for tree ensembles"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "a unified approach to interpreting model predictions"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep learning face attributes in the wild"
    - "bounding the estimation error of sampling based shapley value approximation"

Connected through hyperedge HE-6 (induced by source paper "zorro a model agnostic system to price consumer data"):
  with members:
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "real time bidding benchmarking with ipinyou dataset"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-7 (induced by source paper "collaborative machine learning markets with data replication robust payments"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "model based pricing for machine learning in a data marketplace"
    - "membership inference attacks against machine learning models"
    - "a collaborative mechanism for crowdsourcing prediction problems"

Connected through hyperedge HE-8 (induced by source paper "towards standardization of data licenses the montreal data license"):
  with members:
    - "datasheets for datasets"

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)