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 traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
  Abstract: The traveling salesman problem (TSP) is among the most famous NP-hard optimization problems. We design for this problem an algorithm that for any fixed $\varepsilon>0$ computes in randomized polynomial time a $(1+\varepsilon)$-approximation to the optimal tour in TSP instances that form an arbitrary metric space with bounded intrinsic dimension. The celebrated results of Arora [J. ACM, 45 (1998), pp. 753--782] and Mitchell [SIAM J. Comput., 28 (1999), pp. 1298--1309] prove that the above result holds in the special case of TSP in a fixed-dimensional Euclidean space. Thus, our algorithm demonstrates that the algorithmic tractability of metric TSP depends on the dimensionality of the space and not on its specific geometry. This result resolves a problem that has been open since the quasi-polynomial time algorithm of Talwar [Proceedings of the 36th Annual ACM Symposium on Theory of Computing, 2004, pp. 281--290].

Connected through hyperedge HE-1 (induced by source paper "a quasi polynomial algorithm for well spaced hyperbolic tsp"):
  with members:
    - "a framework for eth tight algorithms and lower bounds in geometric intersection graphs"
    - "an eth tight exact algorithm for euclidean tsp"
    - "hyperbolic intersection graphs and quasi polynomial time"
    - "new inapproximability bounds for tsp"

Connected through hyperedge HE-2 (induced by source paper "travelling on graphs with small highway dimension"):
  with members:
    - "hierarchy of transportation network parameters and hardness results"
    - "the parameterized hardness of the k center problem in transportation networks"
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"
    - "structural parameters tight bounds and approximation for k r center"
    - "beyond highway dimension small distance labels using tree skeletons"
    - "solving generalized maximum weight connected subgraph problem for network enrichment analysis"
    - "fixed parameter approximations for k center problems in low highway dimension graphs"
    - "efficient activity detection in untrimmed video with max subgraph search"

Connected through hyperedge HE-3 (induced by source paper "near linear time approximation schemes for steiner tree and forest in low dimensional spaces"):
  with members:
    - "greedy spanners are optimal in doubling metrics"
    - "the greedy spanner is existentially optimal"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "a light metric spanner"
    - "a polynomial time approximation scheme for euclidean steiner forest"
    - "an efficient polynomial time approximation scheme for steiner forest in planar graphs"
    - "approximation schemes for steiner forest on planar graphs and graphs of bounded treewidth"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-4 (induced by source paper "near linear time approximation schemes for clustering in doubling metrics"):
  with members:
    - "travelling on graphs with small highway dimension"
    - "the parameterized hardness of the k center problem in transportation networks"
    - "approximation schemes for capacitated clustering in doubling metrics"
    - "a fast approximation scheme for low dimensional k means"
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"

Connected through hyperedge HE-5 (induced by source paper "varepsilon coresets for clustering with outliers in doubling metrics"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "clustering high dimensional dynamic data streams"
    - "coresets and sketches"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "new frameworks for offline and streaming coreset constructions"
    - "local search yields a ptas for k means in doubling metrics"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-6 (induced by source paper "a ptas for euclidean tsp with hyperplane neighborhoods"):
  with members:
    - "chasing convex bodies optimally"
    - "competitively chasing convex bodies"
    - "a ptas for tsp with neighborhoods among fat regions in the plane"
    - "nested convex bodies are chaseable"
    - "the traveling salesman problem for lines balls and planes"
    - "the traveling salesman problem for lines and rays in the plane"
    - "approximation algorithms for tsp with neighborhoods in the plane"

Connected through hyperedge HE-7 (induced by source paper "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"):
  with members:
    - "beyond highway dimension small distance labels using tree skeletons"
    - "fixed parameter approximations for k center problems in low highway dimension graphs"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "lower bounds for embedding into distributions over excluded minor graph families"

Connected through hyperedge HE-8 (induced by source paper "an upper bound for min max angle of polygons"):
  with members:
    - "counting and enumerating crossing free geometric graphs"
    - "a 4 5 approximation algorithm for the maximum traveling salesman problem"
    - "a bound on a convexity measure for point sets"

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)