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: "learning valuation functions"
  Abstract: In this paper we study the approximate learnability of valuations commonly used throughout economics and game theory for the quantitative encoding of agent preferences. We provide upper and lower bounds regarding the learnability of important subclasses of valuation functions that express no-complementarities. Our main results concern their approximate learnability in the distributional learning (PAC-style) setting. We provide nearly tight lower and upper bounds of $\tilde{\Theta}(n^{1/2})$ on the approximation factor for learning XOS and subadditive valuations, both widely studied superclasses of submodular valuations. Interestingly, we show that the $\tilde{\Omega}(n^{1/2})$ lower bound can be circumvented for XOS functions of polynomial complexity; we provide an algorithm for learning the class of XOS valuations with a representation of polynomial size achieving an $O(n^{\eps})$ approximation factor in time $O(n^{1/\eps})$ for any $\eps > 0$. This highlights the importance of considering the complexity of the target function for polynomial time learning. We also provide new learning results for interesting subclasses of submodular functions. #R##N#Our upper bounds for distributional learning leverage novel structural results for all these valuation classes. We show that many of these results provide new learnability results in the Goemans et al. model (SODA 2009) of approximate learning everywhere via value queries. #R##N#We also introduce a new model that is more realistic in economic settings, in which the learner can set prices and observe purchase decisions at these prices rather than observing the valuation function directly. In this model, most of our upper bounds continue to hold despite the fact that the learner receives less information (both for learning in the distributional setting and with value queries), while our lower bounds naturally extend.

Connected through hyperedge HE-1 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the limitations of representing functions on sets"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "simplicial closure and higher order link prediction"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-2 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-3 (induced by source paper "submodular cost submodular cover with an approximate oracle"):
  with members:
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "cost effective seed selection in online social networks"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "submodular optimization under noise"
    - "an efficient streaming algorithm for the submodular cover problem"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"

Connected through hyperedge HE-4 (induced by source paper "envy free classification"):
  with members:
    - "probably approximately metric fair learning"
    - "a voting based system for ethical decision making"
    - "learning non discriminatory predictors"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "discrimination in online ad delivery"
    - "multiclass learning approaches a theoretical comparison with implications"

Connected through hyperedge HE-5 (induced by source paper "combinatorial auctions do need modest interaction"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "on simultaneous two player combinatorial auctions"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "computational efficiency requires simple taxation"
    - "welfare maximization with limited interaction"
    - "combinatorial auctions via posted prices"
    - "economic efficiency requires interaction"
    - "simultaneous auctions are almost efficient"

Connected through hyperedge HE-6 (induced by source paper "statistical cost sharing"):
  with members:
    - "the sample complexity of revenue maximization"
    - "learning cooperative games"
    - "the limitations of optimization from samples"
    - "sampling and representation complexity of revenue maximization"
    - "mechanism design for data science"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "intriguing properties of neural networks"
    - "optimal bounds on approximation of submodular and xos functions by juntas"

Connected through hyperedge HE-7 (induced by source paper "submodular optimization under noise"):
  with members:
    - "when are welfare guarantees robust"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "budget feasible mechanisms for experimental design"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "the computational complexity of truthfulness in combinatorial auctions"

Connected through hyperedge HE-8 (induced by source paper "multidimensional dynamic pricing for welfare maximization"):
  with members:
    - "do prices coordinate markets"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "combinatorial auctions via posted prices"
    - "bandits with knapsacks"
    - "efficiently learning from revealed preference"

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)