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: "optimal lower bounds for universal relation samplers and finding duplicates"
  Abstract: In the communication problem $\mathbf{UR}$ (universal relation) [KRW95], Alice and Bob respectively receive $x$ and $y$ in $\{0,1\}^n$ with the promise that $x\neq y$. The last player to receive a message must output an index $i$ such that $x_i\neq y_i$. We prove that the randomized one-way communication complexity of this problem in the public coin model is exactly $\Theta(\min\{n, \log(1/\delta)\log^2(\frac{n}{\log(1/\delta)})\})$ bits for failure probability $\delta$. Our lower bound holds even if promised $\mathop{support}(y)\subset \mathop{support}(x)$. As a corollary, we obtain optimal lower bounds for $\ell_p$-sampling in strict turnstile streams for $0\le p < 2$, as well as for the problem of finding duplicates in a stream. Our lower bounds do not need to use large weights, and hold even if it is promised that $x\in\{0,1\}^n$ at all points in the stream. #R##N#Our lower bound demonstrates that any algorithm $\mathcal{A}$ solving sampling problems in turnstile streams in low memory can be used to encode subsets of $[n]$ of certain sizes into a number of bits below the information theoretic minimum. Our encoder makes adaptive queries to $\mathcal{A}$ throughout its execution, but done carefully so as to not violate correctness. This is accomplished by injecting random noise into the encoder's interactions with $\mathcal{A}$, which is loosely motivated by techniques in differential privacy. Our correctness analysis involves understanding the ability of $\mathcal{A}$ to correctly answer adaptive queries which have positive but bounded mutual information with $\mathcal{A}$'s internal randomness, and may be of independent interest in the newly emerging area of adaptive data analysis with a theoretical computer science lens.

Connected through hyperedge HE-1 (induced by source paper "tight query complexity lower bounds for pca via finite sample deformed wigner law"):
  with members:
    - "lifting randomized query complexity to randomized communication complexity"
    - "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-2 (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"
    - "lifting randomized query complexity to randomized communication complexity"
    - "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-3 (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"
    - "lifting randomized query complexity to randomized communication complexity"
    - "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-4 (induced by source paper "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "densest subgraph in dynamic graph streams"
    - "dynamic graph connectivity with improved worst case update time and sublinear space"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "an improved randomized data structure for dynamic graph connectivity"
    - "maximum matching in turnstile streams"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"

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)