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: "a converse to banach s fixed point theorem and its cls completeness"
  Abstract: Banach's fixed point theorem for contraction maps has been widely used to analyze the convergence of iterative methods in non-convex problems. It is a common experience, however, that iterative maps fail to be globally contracting under the natural metric in their domain, making the applicability of Banach's theorem limited. We explore how generally we can apply Banach's fixed point theorem to establish the convergence of iterative methods when pairing it with carefully designed metrics. #R##N#Our first result is a strong converse of Banach's theorem, showing that it is a universal analysis tool for establishing global convergence of iterative methods to unique fixed points, and for bounding their convergence rate. In other words, we show that, whenever an iterative map globally converges to a unique fixed point, there exists a metric under which the iterative map is contracting and which can be used to bound the number of iterations until convergence. We illustrate our approach in the widely used power method, providing a new way of bounding its convergence rate through contraction arguments. #R##N#We next consider the computational complexity of Banach's fixed point theorem. Making the proof of our converse theorem constructive, we show that computing a fixed point whose existence is guaranteed by Banach's fixed point theorem is CLS-complete. We thus provide the first natural complete problem for the class CLS, which was defined in [Daskalakis, Papadimitriou 2011] to capture the complexity of problems such as P-matrix LCP, computing KKT-points, and finding mixed Nash equilibria in congestion and network coordination games.

Connected through hyperedge HE-1 (induced by source paper "ppp completeness with connections to cryptography"):
  with members:
    - "on the polynomial parity argument complexity of the combinatorial nullstellensatz"
    - "consensus halving is ppa complete"
    - "cls new problems and completeness"
    - "local max cut in smoothed polynomial time"
    - "integer factoring and modular square roots"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "on the complexity of nash equilibria in anonymous games"

Connected through hyperedge HE-2 (induced by source paper "spectral inference networks unifying deep and spectral learning"):
  with members:
    - "eigenoption discovery through the deep successor representation"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "connecting generative adversarial networks and actor critic methods"
    - "successor features for transfer in reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "spectral networks and locally connected networks on graphs"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "spectral inference networks unifying spectral methods with deep learning"):
  with members:
    - "eigenoption discovery through the deep successor representation"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "connecting generative adversarial networks and actor critic methods"
    - "the arcade learning environment an evaluation platform for general agents"
    - "spectral networks and locally connected networks on graphs"
    - "a spectral algorithm for latent dirichlet allocation"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

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.CC(Computational Complexity)