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: "correlation clustering in data streams"
  Abstract: Clustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as $k$-center, $k$-median, and $k$-means. Such algorithms need to be both time and and space efficient. In this paper, we address the problem of correlation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on $n$ nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in different clusters whereas the end-points of positive-weight edges are typically in the same cluster. We present polynomial-time, $O(n\cdot \ \mbox{polylog}~n)$-space approximation algorithms for natural problems that arise. #R##N#We first develop data structures based on linear sketches that allow the "quality" of a given node-partition to be measured. We then combine these data structures with convex programming and sampling techniques to solve the relevant approximation problem. Unfortunately, the standard LP and SDP formulations are not obviously solvable in $O(n\cdot \mbox{polylog}~n)$-space. Our work presents space-efficient algorithms for the convex programming required, as well as approaches to reduce the adaptivity of the sampling.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (induced by source paper "fully dynamic maximal independent set with polylogarithmic update time"):
  with members:
    - "fully dynamic maximal independent set in expected poly log update time"
    - "exponentially faster massively parallel maximal matching"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "improved algorithms for fully dynamic maximal independent set"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"

Connected through hyperedge HE-3 (induced by source paper "exponentially faster massively parallel maximal matching"):
  with members:
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "fully dynamic matching beating 2 approximation in delta epsilon update time"):
  with members:
    - "exponentially faster massively parallel maximal matching"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms"
    - "deterministic fully dynamic approximate vertex cover and fractional matching in o 1 amortized update time"
    - "fully dynamic maximal matching in constant update time"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "deterministic fully dynamic data structures for vertex cover and matching"

Connected through hyperedge HE-5 (induced by source paper "improved massively parallel computation algorithms for mis matching and vertex cover"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "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"
    - "derandomizing local distributed algorithms under bandwidth restrictions"
    - "deterministic mst sparsification in the congested clique"
    - "algebraic methods in the congested clique"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"

Connected through hyperedge HE-6 (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  with members:
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "sketching cuts in graphs and hypergraphs"
    - "the sketching complexity of graph cuts"
    - "streaming lower bounds for approximating max cut"
    - "better bounds for matchings in the streaming model"
    - "a note on no need to choose how to get both a ptas and sublinear query complexity"
    - "single pass sparsification in the streaming model with edge deletions"
    - "graph sparsification via refinement sampling"

Connected through hyperedge HE-7 (induced by source paper "small space stream summary for matroid center"):
  with members:
    - "streaming algorithms for submodular function maximization"
    - "submodular maximization meets streaming matchings matroids and more"
    - "buyback problem approximate matroid intersection with cancellation costs"

Connected through hyperedge HE-8 (induced by source paper "independent sets in vertex arrival streams"):
  with members:
    - "interval selection in the streaming model"
    - "space constrained interval selection"

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.DS(Data Structures and Algorithms)