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: "towards efficient data valuation based on the shapley value"
  Abstract: How much is my data worth? is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires exponential time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "a distributional framework for data valuation"):
  with members:
    - "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 marketplace for data an algorithmic solution"
    - "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 "estimating training data influence by tracking gradient descent"):
  with members:
    - "representer point selection for explaining deep neural networks"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "character level convolutional networks for text classification"
    - "deep residual learning for image recognition"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-3 (induced by source paper "fedcoin a peer to peer payment system for federated learning"):
  with members:
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "advances and open problems in federated learning"
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "baffle blockchain based aggregator free federated learning"
    - "federated machine learning concept and applications"
    - "blockchained on device federated learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "majority is not enough bitcoin mining is vulnerable"

Connected through hyperedge HE-4 (induced by source paper "efficient task specific data valuation for nearest neighbor algorithms"):
  with members:
    - "data markets to support ai for all pricing valuation and governance"
    - "model based pricing for machine learning in a data marketplace"
    - "databright towards a global exchange for decentralized data ownership and trusted computation"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "bounding the estimation error of sampling based shapley value approximation"

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:
    - "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 marketplace for data an algorithmic solution"
    - "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 "mapping the potential and pitfalls of data dividends as a means of sharing the profits of artificial intelligence"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "low cost learning via active data procurement"
    - "incentivizing high quality crowdwork"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-7 (induced by source paper "rewarding high quality data via influence functions"):
  with members:
    - "competitive statistical estimation with strategic data sources"
    - "federated learning strategies for improving communication efficiency"
    - "strategyproof linear regression in high dimensions"
    - "optimal data acquisition for statistical estimation"
    - "statistical estimation with strategic data sources in competitive settings"
    - "truthful linear regression"
    - "approval voting and incentives in crowdsourcing"

Connected through hyperedge HE-8 (induced by source paper "on the accuracy of influence functions for measuring group effects"):
  with members:
    - "contamination attacks and mitigation in multi party machine learning"
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "understanding the origins of bias in word embeddings"
    - "interpreting black box predictions using fisher kernels"
    - "certified defenses for data poisoning attacks"
    - "a broad coverage challenge corpus for sentence understanding through 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.LG(Machine Learning)