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: "fast balanced partitioning is hard even on grids and trees"
  Abstract: Two kinds of approximation algorithms exist for the k-BALANCED PARTITIONING problem: those that are fast but compute unsatisfying approximation ratios, and those that guarantee high quality ratios but are slow. In this paper we prove that this tradeoff between runtime and solution quality is necessary. For the problem a minimum number of edges in a graph need to be found that, when cut, partition the vertices into k equal-sized sets. We develop a reduction framework which identifies some necessary conditions on the considered graph class in order to prove the hardness of the problem. We focus on two combinatorially simple but very different classes, namely trees and solid grid graphs. The latter are finite connected subgraphs of the infinite 2D grid without holes. First we use the framework to show that for solid grid graphs it is NP-hard to approximate the optimum number of cut edges within any satisfying ratio. Then we consider solutions in which the sets may deviate from being equal-sized. Our framework is used on grids and trees to prove that no fully polynomial time algorithm exists that computes solutions in which the sets are arbitrarily close to equal-sized. This is true even if the number of edges cut is allowed to increase the more stringent the limit on the set sizes is. These are the first bicriteria inapproximability results for the problem.

Connected through hyperedge HE-1 (induced by source paper "towards balanced clustering part 1 preliminaries"):
  with members:
    - "fixed parameter approximations for k center problems in low highway dimension graphs"
    - "on the parameterized complexity of computing balanced partitions in graphs"
    - "towards combinatorial clustering preliminary research survey"
    - "multiset estimates and combinatorial synthesis"
    - "recent advances in graph partitioning"
    - "balanced allocation on graphs"
    - "a complete anytime algorithm for balanced number partitioning"

Connected through hyperedge HE-2 (induced by source paper "on the parameterized complexity of computing balanced partitions in graphs"):
  with members:
    - "expanding the expressive power of monadic second order logic on restricted graph classes"
    - "minimum bisection is fixed parameter tractable"
    - "kernelization lower bounds by cross composition"
    - "finding small separators in linear time via treewidth reduction"
    - "cross composition a new technique for kernelization lower bounds"
    - "treewidth reduction for constrained separation and bipartization problems"

Connected through hyperedge HE-3 (induced by source paper "parameterized complexity of asynchronous border minimization"):
  with members:
    - "hardness and approximation of the asynchronous border minimization problem"

Connected through hyperedge HE-4 (induced by source paper "efficient large scale graph processing on hybrid cpu and gpu systems"):
  with members:
    - "mathematical foundations of computer science 2012"

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)