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: "lifting randomized query complexity to randomized communication complexity"
  Abstract: We show that for any (partial) query function $f:\{0,1\}^n\rightarrow \{0,1\}$, the randomized communication complexity of $f$ composed with $\mathrm{Index}^n_m$ (with $m= \mathrm{poly}(n)$) is at least the randomized query complexity of $f$ times $\log n$. Here $\mathrm{Index}_m : [m] \times \{0,1\}^m \rightarrow \{0,1\}$ is defined as $\mathrm{Index}_m(x,y)= y_x$ (the $x$th bit of $y$). #R##N#Our proof follows on the lines of Raz and Mckenzie [RM99] (and its generalization due to [GPW15]), who showed a lifting theorem for deterministic query complexity to deterministic communication complexity. Our proof deviates from theirs in an important fashion that we consider partitions of rectangles into many sub-rectangles, as opposed to a particular sub-rectangle with desirable properties, as considered by Raz and McKenzie. As a consequence of our main result, some known separations between quantum and classical communication complexities follow from analogous separations in the query world.

Connected through hyperedge HE-1 (induced by source paper "complexity theory game theory and economics the barbados lectures"):
  with members:
    - "a round collapse theorem for computationally sound protocols or tfnp is hard on average in pessiland"
    - "on communication complexity of fixed point computation"
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "query to communication lifting for bpp"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "the price of anarchy in auctions"
    - "communication complexity for algorithm designers"

Connected through hyperedge HE-2 (induced by source paper "tight query complexity lower bounds for pca via finite sample deformed wigner law"):
  with members:
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "how to escape saddle points efficiently"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "lower bounds for adaptive sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "on the randomized complexity of minimizing a convex quadratic function"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "mutual information in rank one matrix estimation"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "lower bounds for adaptive sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "the communication complexity of local search"):
  with members:
    - "combinatorial auctions with endowment effect"
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "query to communication lifting for bpp"
    - "communication complexity of approximate nash equilibria"
    - "communication lower bounds via critical block sensitivity"

Connected through hyperedge HE-5 (induced by source paper "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "how to escape saddle points efficiently"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "low rank approximation with entrywise ell_1 norm error"
    - "a geometric analysis of phase retrieval"

Connected through hyperedge HE-6 (induced by source paper "quantum communication query tradeoffs"):
  with members:
    - "query to communication lifting for bpp"
    - "a discrepancy lower bound for information complexity"
    - "an approximation algorithm for approximation rank"
    - "communication complexity lower bounds by polynomials"

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)