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: "the mechanics of n player differentiable games"
  Abstract: The cornerstone underpinning deep learning is the guarantee that gradient descent on an objective converges to local minima. Unfortunately, this guarantee fails in settings, such as generative adversarial nets, where there are multiple interacting losses. The behavior of gradient-based methods in games is not well understood -- and is becoming increasingly important as adversarial and multi-objective architectures proliferate. In this paper, we develop new techniques to understand and control the dynamics in general games. The key result is to decompose the second-order dynamics into two components. The first is related to potential games, which reduce to gradient descent on an implicit function; the second relates to Hamiltonian games, a new class of games that obey a conservation law, akin to conservation laws in classical mechanical systems. The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in general games. Basic experiments show SGA is competitive with recently proposed algorithms for finding local Nash equilibria in GANs -- whilst at the same time being applicable to -- and having guarantees in -- much more general games.

Connected through hyperedge HE-1 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners"):
  with members:
    - "logan latent optimisation for generative adversarial networks"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "differentiable game mechanics"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "the numerics of gans"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization"):
  with members:
    - "from darwin to poincar e and von neumann recurrence and cycles in evolutionary and algorithmic game theory"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "a theory of regularized markov decision processes"
    - "openspiel a framework for reinforcement learning in games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "stable opponent shaping in differentiable games"
    - "modeling friends and foes"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-3 (induced by source paper "understanding and mitigating gradient pathologies in physics informed neural networks"):
  with members:
    - "machine learning in cardiovascular flows modeling predicting arterial blood pressure from non invasive 4d flow mri data using physics informed neural networks"
    - "variational physics informed neural networks for solving partial differential equations"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "covariant compositional networks for learning graphs"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "attention is all you need"
    - "cycles in adversarial regularized learning"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-4 (induced by source paper "a mean field analysis of two player zero sum games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "local saddle point optimization a curvature exploitation approach"
    - "deep clustering for unsupervised learning of visual features"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "multi agent diverse generative adversarial networks"
    - "an online learning approach to generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "a unified analysis of gradient based methods for a whole spectrum of games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"
    - "training gans with optimism"

Connected through hyperedge HE-6 (induced by source paper "local nash equilibria are isolated strict local nash equilibria in almost all zero sum continuous games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a variational inequality perspective on generative adversarial nets"
    - "robust adversarial reinforcement learning"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"

Connected through hyperedge HE-7 (induced by source paper "accelerating smooth games by manipulating spectral shapes"):
  with members:
    - "a tight and unified analysis of extragradient for a whole spectrum of differentiable games"
    - "differentiable game mechanics"
    - "a variational inequality perspective on generative adversarial nets"
    - "the numerics of gans"
    - "training gans with optimism"
    - "stochastic variance reduction methods for saddle point problems"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of the set of local maxima"):
  with members:
    - "deep anomaly detection using geometric transformations"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "understanding deep neural networks with rectified linear units"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning deep representations of appearance and motion for anomalous event detection"

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)