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: "efficient computation of the shapley value for game theoretic network centrality"
  Abstract: The Shapley value--probably the most important normative payoff division scheme in coalitional games--has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.

Connected through hyperedge HE-1 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "open ended learning in symmetric zero sum games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "maximizing influence based group shapley centrality"):
  with members:
    - "game theoretic network centrality a review"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scaling up group closeness maximization"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "axioms for centrality"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-3 (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"
    - "towards efficient data valuation based on the shapley value"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "shapley q value a local reward approach to solve global reward games"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "learning to teach in cooperative multiagent reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-5 (induced by source paper "machine learning in network centrality measures tutorial and outlook"):
  with members:
    - "an analysis of centrality measures for complex and social networks"
    - "a scalable generative graph model with community structure"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "computing classic closeness centrality at scale"
    - "representation learning a review and new perspectives"
    - "community structure and scale free collections of erdős renyi graphs"
    - "defining and evaluating network communities based on ground truth"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-6 (induced by source paper "attacking power indices by manipulating player reliability"):
  with members:
    - "h α an index to quantify an individual s scientific leadership"
    - "prestige drives epistemic inequality in the diffusion of scientific ideas"
    - "hiding individuals and communities in a social network"
    - "game theoretic network centrality a review"
    - "cooperative games with bounded dependency degree"
    - "solving cooperative reliability games"
    - "false name manipulations in weighted voting games"
    - "cloning in elections finding the possible winners"

Connected through hyperedge HE-7 (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 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"

Connected through hyperedge HE-8 (induced by source paper "strategic attack defense in security diffusion games"):
  with members:
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "multidefender security games"
    - "stochastic games for security in networks with interdependent nodes"
    - "reinforcement learning a survey"

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)