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: "optimal auctions through deep learning"
  Abstract: Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30-40 years of intense research the problem remains unsolved for settings with two or more items. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard machine learning pipelines. We prove generalization bounds and present extensive experiments, recovering essentially all known analytical solutions for multi-item settings, and obtaining novel mechanisms for settings in which the optimal mechanism is unknown.

Connected through hyperedge HE-1 (induced by source paper "bittensor an intermodel intelligence measure"):
  with members:
    - "learning home crowdsourced training of large neural networks using decentralized mixture of experts"
    - "smooth markets a basic mechanism for organizing gradient based learners"
    - "on the measure of intelligence"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-2 (induced by source paper "estimating approximate incentive compatibility"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a b testing of auctions"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-3 (induced by source paper "learning to bid in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "learning to bid without knowing your value"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-4 (induced by source paper "toward an automated auction framework for wireless federated learning services market"):
  with members:
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "agnostic federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "fair resource allocation in federated learning"
    - "joint service pricing and cooperative relay communication for federated learning"
    - "federated learning with non iid data"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-5 (induced by source paper "robust multi agent counterfactual prediction"):
  with members:
    - "theory of minds understanding behavior in groups through inverse planning"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "reinforcement learning and inverse reinforcement learning with system 1 and system 2"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning from self play in imperfect information games"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-6 (induced by source paper "robust stackelberg buyers in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of revenue maximization"
    - "making the most of your samples"

Connected through hyperedge HE-7 (induced by source paper "auction based charging scheduling with deep learning framework for multi drone networks"):
  with members:
    - "decentralized combinatorial auctions for multi unit resource allocation"
    - "auction mechanisms in cloud fog computing resource allocation for public blockchain networks"
    - "optimal auction for edge computing resource management in mobile blockchain networks a deep learning approach"
    - "least squares generative adversarial networks"
    - "a distributed auctioneer for resource allocation in decentralized systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "learning optimal redistribution mechanisms through neural networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"
    - "redistribution mechanisms for assignment of heterogeneous objects"
    - "optimal mechansim design and money burning"

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)