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: "new inapproximability bounds for tsp"
  Abstract: In this paper, we study the approximability of the metric Traveling Salesman Problem (TSP) and prove new explicit inapproximability bounds for that problem. The best up to now known hardness of approximation bounds were 185/184 for the symmetric case (due to Lampis) and 117/116 for the asymmetric case (due to Papadimitriou and Vempala). We construct here two new bounded occurrence CSP reductions which improve these bounds to 123/122 and 75/74, respectively. The latter bound is the first improvement in more than a decade for the case of the asymmetric TSP. One of our main tools, which may be of independent interest, is a new construction of a bounded degree wheel amplifier used in the proof of our results.

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"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"

Connected through hyperedge HE-2 (induced by source paper "hard to solve instances of the euclidean traveling salesman problem"):
  with members:
    - "on the integrality ratio of the subtour lp for euclidean tsp"

Connected through hyperedge HE-3 (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"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"

Connected through hyperedge HE-4 (induced by source paper "reducing path tsp to tsp"):
  with members:
    - "beating the integrality ratio for s t tours in graphs"
    - "a 1 5 approximation for path tsp"
    - "better s t tours by gao trees"
    - "reassembling trees for the traveling salesman"
    - "eight fifth approximation for the path tsp"
    - "approximating minimum cost connected t joins"
    - "improving christofides algorithm for the s t path tsp"
    - "13 9 approximation for graphic tsp"

Connected through hyperedge HE-5 (induced by source paper "characterizing the integrality gap of the subtour lp for the circulant traveling salesman problem"):
  with members:
    - "semidefinite programming relaxations of the traveling salesman problem and their integrality gaps"
    - "the unbounded integrality gap of a semidefinite relaxation of the traveling salesman problem"
    - "on approximation lower bounds for tsp with bounded metrics"
    - "13 9 approximation for graphic tsp"

Connected through hyperedge HE-6 (induced by source paper "from symmetry to asymmetry generalizing tsp approximations by parametrization"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "constant factor approximations for asymmetric tsp on nearly embeddable graphs"

Connected through hyperedge HE-7 (induced by source paper "semidefinite programming relaxations of the traveling salesman problem and their integrality gaps"):
  with members:
    - "the unbounded integrality gap of a semidefinite relaxation of the traveling salesman problem"

Connected through hyperedge HE-8 (induced by source paper "on approximate data reduction for the rural postman problem theory and experiments"):
  with members:
    - "parameterized algorithms and data reduction for the short secluded s t path problem"
    - "revisiting connected vertex cover fpt algorithms and lossy kernels"
    - "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments"
    - "polynomial kernels for weighted problems"
    - "constant factor approximations for capacitated arc routing without triangle inequality"
    - "fixed parameter algorithms for minimum cost edge connectivity augmentation"

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)