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: "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
  Abstract: We show that the integrality gap of the natural LP relaxation of the Asymmetric Traveling Salesman Problem is $\text{polyloglog}(n)$. In other words, there is a polynomial time algorithm that approximates the value of the optimum tour within a factor of $\text{polyloglog}(n)$, where $\text{polyloglog}(n)$ is a bounded degree polynomial of $\log\log(n)$. We prove this by showing that any $k$-edge-connected unweighted graph has a $\text{polyloglog}(n)/k$-thin spanning tree. #R##N#Our main new ingredient is a procedure, albeit an exponentially sized convex program, that "transforms" graphs that do not admit any spectrally thin trees into those that provably have spectrally thin trees. More precisely, given a $k$-edge-connected graph $G=(V,E)$ where $k\geq 7\log(n)$, we show that there is a matrix $D$ that "preserves" the structure of all cuts of $G$ such that for a set $F\subseteq E$ that induces an $\Omega(k)$-edge-connected graph, the effective resistance of every edge in $F$ w.r.t. $D$ is at most $\text{polylog}(k)/k$. Then, we use a recent extension of the seminal work of Marcus, Spielman, and Srivastava [MSS13] by the authors [AO14] to prove the existence of a $\text{polylog}(k)/k$-spectrally thin tree with respect to $D$. Such a tree is $\text{polylog}(k)/k$-combinatorially thin with respect to $G$ as $D$ preserves the structure of cuts of $G$.

Connected through hyperedge HE-1 (induced by source paper "a spectral approach to network design"):
  with members:
    - "network design for s t effective resistance"
    - "new notions and constructions of sparsification for graphs and hypergraphs"
    - "on a generalization of iterated and randomized rounding"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "graph clustering using effective resistance"

Connected through hyperedge HE-2 (induced by source paper "electrical flows over spanning trees"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "sampling random spanning trees faster than matrix multiplication"
    - "computing maximum flow with augmenting electrical flows"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "graph sparsification by effective resistances"
    - "nearly tight low stretch spanning trees"

Connected through hyperedge HE-3 (induced by source paper "network design for s t effective resistance"):
  with members:
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "parameters of two prover one round game and the hardness of connectivity problems"
    - "iterative rounding approximation algorithms for degree bounded node connectivity network design"
    - "approximating minimum cost k node connected subgraphs via independence free graphs"
    - "reductions between expansion problems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-4 (induced by source paper "classification in asymmetric spaces via sample compression"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "maximum margin multiclass nearest neighbors"
    - "efficient classification for metric data"
    - "quasi metrics similarities and searches aspects of geometry of protein datasets"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-5 (induced by source paper "an improved approximation algorithm for atsp"):
  with members:
    - "the asymmetric traveling salesman path lp has constant integrality ratio"
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "constant factor approximation for atsp with two edge weights"
    - "approximating atsp by relaxing connectivity"

Connected through hyperedge HE-6 (induced by source paper "on a generalization of iterated and randomized rounding"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "algorithmic discrepancy beyond partial coloring"
    - "approximation friendly discrepancy rounding"
    - "pipage rounding pessimistic estimators and matrix concentration"
    - "entropy optimization and counting"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-7 (induced by source paper "belief propagation bethe approximation and polynomials"):
  with members:
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "real stable polynomials and matroids optimization and counting"
    - "the bethe permanent of a nonnegative matrix"
    - "entropy optimization and counting"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "the bethe partition function of log supermodular graphical models"
    - "partition functions of normal factor graphs"
    - "belief propagation and loop calculus for the permanent of a non negative matrix"

Connected through hyperedge HE-8 (induced by source paper "a constant factor approximation algorithm for the asymmetric traveling salesman problem"):
  with members:
    - "the asymmetric traveling salesman path lp has constant integrality ratio"
    - "constant factor approximation for atsp with two edge weights"
    - "approximating atsp by relaxing connectivity"
    - "new inapproximability bounds for tsp"
    - "shorter tours by nicer ears 7 5 approximation for graphic tsp 3 2 for the path version and 4 3 for two edge connected subgraphs"
    - "improving christofides algorithm for the s t path tsp"
    - "13 9 approximation for graphic tsp"
    - "the asymmetric traveling salesman problem on graphs with bounded genus"

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)