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: "the sketching complexity of graph cuts"
  Abstract: We study the problem of sketching an input graph, so that given the sketch, one can estimate the weight of any cut in the graph within factor $1+\epsilon$. We present lower and upper bounds on the size of a randomized sketch, focusing on the dependence on the accuracy parameter $\epsilon>0$. #R##N#First, we prove that for every $\epsilon > 1/\sqrt n$, every sketch that succeeds (with constant probability) in estimating the weight of all cuts $(S,\bar S)$ in an $n$-vertex graph (simultaneously), must be of size $\Omega(n/\epsilon^2)$ bits. In the special case where the sketch is itself a weighted graph (which may or may not be a subgraph) and the estimator is the sum of edge weights across the cut in the sketch, i.e., a cut sparsifier, we show the sketch must have $\Omega(n/\epsilon^2)$ edges, which is optimal. Despite the long sequence of work on graph sparsification, no such lower bound was known on the size of a cut sparsifier. #R##N#We then design a randomized sketch that, given $\epsilon\in(0,1)$ and an edge-weighted $n$-vertex graph, produces a sketch of size $\tilde O(n/\epsilon)$ bits, from which the weight of any cut $(S,\bar S)$ can be reported, with high probability, within factor $1+\epsilon$. The previous upper bound is $\tilde O(n/\epsilon^2)$ bits, which follows by storing a cut sparsifier (Bencz{\'u}r and Karger, 1996). To obtain this improvement, we critically use both that the sketch need only be correct on each fixed cut with high probability (rather than on all cuts), and that the estimation procedure of the data structure can be arbitrary (rather than a weighted subgraph). We also show a lower bound of $\Omega(n/\epsilon)$ bits for the space requirement of any data structure achieving this guarantee.

Connected through hyperedge HE-1 (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  with members:
    - "correlation clustering in data streams"
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "sketching cuts in graphs and hypergraphs"
    - "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-2 (induced by source paper "sparsification of two variable valued csps"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "on sketching quadratic forms"
    - "sketching cuts in graphs and hypergraphs"
    - "a sketching algorithm for spectral graph sparsification"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "graph sparsification in the semi streaming model"
    - "twice ramanujan sparsifiers"
    - "randomized approximation schemes for cuts and flows in capacitated graphs"

Connected through hyperedge HE-3 (induced by source paper "towards resistance sparsifiers"):
  with members:
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-4 (induced by source paper "space lower bounds for itemset frequency sketches"):
  with members:
    - "optimal lower bound for itemset frequency indicator sketches"
    - "faster private release of marginals on small databases"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "private data release via learning thresholds"
    - "lower bounds in differential privacy"
    - "on finding similar items in a stream of transactions"

Connected through hyperedge HE-5 (induced by source paper "sketching cuts in graphs and hypergraphs"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "streaming lower bounds for approximating max cut"
    - "single pass spectral sparsification in dynamic streams"
    - "single pass sparsification in the streaming model with edge deletions"
    - "graph sparsification in the semi streaming model"
    - "randomized approximation schemes for cuts and flows in capacitated graphs"

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)