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: "what game are we playing end to end learning in normal and extensive form games"
  Abstract: Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents. This paper deals with the relatively under-explored but equally important "inverse" setting, where the parameters of the underlying game are not known to all agents, but must be learned through observations. We propose a differentiable, end-to-end learning framework for addressing this task. In particular, we consider a regularized version of the game, equivalent to a particular form of quantal response equilibrium, and develop 1) a primal-dual Newton method for finding such equilibrium points in both normal and extensive form games; and 2) a backpropagation method that lets us analytically compute gradients of all relevant game parameters through the solution itself. This ultimately lets us learn the game by training in an end-to-end fashion, effectively by integrating a "differentiable game solver" into the loop of larger deep network architectures. We demonstrate the effectiveness of the learning method in several settings including poker and security game tasks.

Connected through hyperedge HE-1 (induced by source paper "differentiable convex optimization layers"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "the limited multi label projection layer"
    - "deep declarative networks a new hope"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "neural proximal gradient descent for compressive imaging"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "sparse and constrained attention for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"):
  with members:
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "deepproblog neural probabilistic logic programming"
    - "learning a sat solver from single bit supervision"
    - "deeplogic towards end to end differentiable logical reasoning"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "recurrent relational networks"

Connected through hyperedge HE-3 (induced by source paper "mipaal mixed integer program as a layer"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "decision focused learning of adversary behavior in security games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "deep reinforcement learning for green security games with real time information"
    - "optnet differentiable optimization as a layer in neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "position indexed formulations for kidney exchange"

Connected through hyperedge HE-4 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "multiagent evaluation under incomplete information"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a generalised method for empirical game theoretic analysis"
    - "bandit learning in concave n person games"
    - "safe and nested subgame solving for imperfect information games"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "decision focused learning of adversary behavior in security games"):
  with members:
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"
    - "predicting human behavior in unrepeated simultaneous move games"
    - "the mysteries of security games equilibrium computation becomes combinatorial algorithm design"
    - "adversarial patrolling with spatially uncertain alarm signals"
    - "learning adversary behavior in security games a pac model perspective"

Connected through hyperedge HE-6 (induced by source paper "large scale learning of agent rationality in two player zero sum games"):
  with members:
    - "theoretical and practical advances on smoothing for extensive form games"
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "computational rationalization the inverse equilibrium problem"

Connected through hyperedge HE-7 (induced by source paper "modeling friends and foes"):
  with members:
    - "the mechanics of n player differentiable games"
    - "ai safety gridworlds"
    - "the numerics of gans"
    - "concrete problems in ai safety"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "intriguing properties of neural networks"
    - "the best of both worlds stochastic and adversarial bandits"
    - "risk sensitive path integral control"

Connected through hyperedge HE-8 (induced by source paper "regret circuits composability of regret minimizers"):
  with members:
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "solving large extensive form games with strategy constraints"
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "extensive form perfect equilibrium computation in two player games"

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)