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: "sub exponential approximation schemes for csps from dense to almost sparse"
  Abstract: It has long been known, since the classical work of (Arora, Karger, Karpinski, JCSS~99), that \MC\ admits a PTAS on dense graphs, and more generally, \kCSP\ admits a PTAS on "dense" instances with $\Omega(n^k)$ constraints. In this paper we extend and generalize their exhaustive sampling approach, presenting a framework for $(1-\eps)$-approximating any \kCSP\ problem in \emph{sub-exponential} time while significantly relaxing the denseness requirement on the input instance. Specifically, we prove that for any constants $\delta \in (0, 1]$ and $\eps > 0$, we can approximate \kCSP\ problems with $\Omega(n^{k-1+\delta})$ constraints within a factor of $(1-\eps)$ in time $2^{O(n^{1-\delta}\ln n /\eps^3)}$. The framework is quite general and includes classical optimization problems, such as \MC, {\sc Max}-DICUT, \kSAT, and (with a slight extension) $k$-{\sc Densest Subgraph}, as special cases. For \MC\ in particular (where $k=2$), it gives an approximation scheme that runs in time sub-exponential in $n$ even for "almost-sparse" instances (graphs with $n^{1+\delta}$ edges). We prove that our results are essentially best possible, assuming the ETH. First, the density requirement cannot be relaxed further: there exists a constant $r   0$, \kSAT\ instances with $O(n^{k-1})$ clauses cannot be approximated within a ratio better than $r$ in time $2^{O(n^{1-\delta})}$. Second, the running time of our algorithm is almost tight \emph{for all densities}. Even for \MC\ there exists $r  \delta >0$, \MC\ instances with $n^{1+\delta}$ edges cannot be approximated within a ratio better than $r$ in time $2^{n^{1-\delta'}}$.

Connected through hyperedge HE-1 (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  with members:
    - "correlation clustering in data streams"
    - "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-2 (induced by source paper "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective"):
  with members:
    - "the vertex sample complexity of free energy is polynomial"
    - "the mean field approximation information inequalities algorithms and complexity"
    - "how to calculate partition functions using convex programming hierarchies provable bounds for variational methods"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "conditioning and covariance on caterpillars"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating csps with global cardinality constraints using sdp hierarchies"

Connected through hyperedge HE-3 (induced by source paper "on subexponential running times for approximating directed steiner tree and related problems"):
  with members:
    - "new tools and connections for exponential time approximation"
    - "analytical approach to parallel repetition"
    - "independent set induced matching and pricing connections and tight subexponential time approximation hardnesses"
    - "exponential time approximation of hard problems"

Connected through hyperedge HE-4 (induced by source paper "low rank approximation with entrywise ell_1 norm error"):
  with members:
    - "optimal cur matrix decompositions"
    - "relative errors for deterministic low rank matrix approximations"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "iterative row sampling"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "computing a nonnegative matrix factorization provably"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-5 (induced by source paper "a birthday repetition theorem and complexity of approximating dense csps"):
  with members:
    - "on the hardness of signaling"
    - "am with multiple merlins"
    - "label cover instances with large girth and the hardness of approximating basic k spanner"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximation algorithms and hardness of the k route cut problem"
    - "subsampling mathematical relaxations and average case complexity"

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)