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 and for samplers and finding duplicates in streams"
  Abstract: In the communication problem $\mathbf{UR}$ (universal relation) [KRW95], Alice and Bob respectively receive $x, y \in\{0,1\}^n$ 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 $\Theta(\min\{n,\log(1/\delta)\log^2(\frac n{\log(1/\delta)})\})$ 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 promised $x\in\{0,1\}^n$ at all points in the stream. #R##N#We give two different proofs of our main result. The first proof 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 second proof is via a novel randomized reduction from Augmented Indexing [MNSW98] which needs to interact with $\mathcal A$ adaptively. To handle the adaptivity we identify certain likely interaction patterns and union bound over them to guarantee correct interaction on all of them. To guarantee correctness, it is important that the interaction hides some of its randomness from $\mathcal A$ in the reduction.

Connected through hyperedge HE-1 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space"):
  with members:
    - "faster spectral sparsification in dynamic streams"
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"
    - "on estimating maximum matching size in graph streams"

Connected through hyperedge HE-2 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"

Connected through hyperedge HE-3 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"

Connected through hyperedge HE-4 (induced by source paper "pseudo deterministic streaming"):
  with members:
    - "reproducibility and pseudo determinism in log space"
    - "a note on unconditional subexponential time pseudo deterministic algorithms for bpp search problems"
    - "pseudo deterministic proofs"
    - "pseudodeterministic constructions in subexponential time"
    - "how robust are linear sketches to adaptive inputs"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-5 (induced by source paper "quotient hash tables efficiently detecting duplicates in streaming data"):
  with members:
    - "bloom filters in adversarial environments"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-6 (induced by source paper "sublinear algorithms for delta 1 vertex coloring"):
  with members:
    - "coloring in graph streams"
    - "brief announcement semi mapreduce meets congested clique"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "independent sets in vertex arrival streams"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"
    - "round compression for parallel matching algorithms"
    - "deterministic distributed edge coloring via hypergraph maximal matching"

Connected through hyperedge HE-7 (induced by source paper "graph sketching against adaptive adversaries applied to the minimum degree algorithm"):
  with members:
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"
    - "a framework for analyzing resparsification algorithms"
    - "fully dynamic all pairs shortest paths with worst case update time revisited"
    - "minimum fill in inapproximability and almost tight lower bounds"
    - "fully dynamic spanners with worst case update time"
    - "approximately counting triangles in sublinear time"
    - "single pass spectral sparsification in dynamic streams"

Connected through hyperedge HE-8 (induced by source paper "data streams with bounded deletions"):
  with members:
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "maximum matching in turnstile streams"
    - "stream sampling for frequency cap statistics"
    - "structure aware sampling flexible and accurate summarization"
    - "lower bounds for sparse recovery"

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)