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: "classical algorithms from quantum and arthur merlin communication protocols"
  Abstract: The polynomial method from circuit complexity has been applied to several fundamental problems and obtains the state-of-the-art running times. As observed in [Alman and Williams, STOC 2017], almost all applications of the polynomial method in algorithm design ultimately rely on certain low-rank decompositions of the computation matrices corresponding to key subroutines. They suggest that making use of low-rank decompositions directly could lead to more powerful algorithms, as the polynomial method is just one way to derive such a decomposition. Inspired by their observation, in this paper, we study another way of systematically constructing low-rank decompositions of matrices which could be used by algorithms. It is known that various types of communication protocols lead to certain low-rank decompositions (e.g., $\mathsf{P}$ protocols/rank, $\mathsf{BQP}$ protocols/approximate rank). These are usually interpreted as approaches for proving communication lower bounds, while in this work we explore the other direction. #R##N#We have the two generic algorithmic applications of communication protocols. The first connection is that a fast $\mathsf{BQP}$ communication protocol for a function $f$ implies a fast deterministic additive approximate counting algorithm for a related pair counting problem. The second connection is that a fast $\mathsf{AM}^{\mathsf{cc}}$ protocol for a function $f$ implies a faster-than-bruteforce algorithm for $f\textsf{-Satisfying-Pair}$. #R##N#We also apply our second connection to shed some light on long-standing open problems in communication complexity. We show that if the Longest Common Subsequence problem admits an efficient $\mathsf{AM}^{\mathsf{cc}}$ protocol, then polynomial-size Formula-$\textsf{SAT}$ admits a $2^{n - n^{1-\delta}}$ time algorithm for any constant $\delta > 0$.

Connected through hyperedge HE-1 (induced by source paper "algorithms and lower bounds for de morgan formulas of low communication leaf gates"):
  with members:
    - "improved pseudorandom generators from pseudorandom multi switching lemmas"
    - "tighter connections between formula sat and shaving logs"
    - "fooling polytopes"
    - "on polynomial approximations to ac 0"
    - "conspiracies between learning algorithms circuit lower bounds and pseudorandomness"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "fooling functions of halfspaces under product distributions"

Connected through hyperedge HE-2 (induced by source paper "is a good representation sufficient for sample efficient reinforcement learning"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "information theoretic considerations in batch reinforcement learning"
    - "a theory of regularized markov decision processes"

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)