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: "reductions between expansion problems"
  Abstract: The Small-Set Expansion Hypothesis (Raghavendra, Steurer, STOC 2010) is a natural hardness assumption concerning the problem of approximating the edge expansion of small sets in graphs. This hardness assumption is closely connected to the Unique Games Conjecture (Khot, STOC 2002). In particular, the Small-Set Expansion Hypothesis implies the Unique Games Conjecture (Raghavendra, Steurer, STOC 2010). #R##N#Our main result is that the Small-Set Expansion Hypothesis is in fact equivalent to a variant of the Unique Games Conjecture. More precisely, the hypothesis is equivalent to the Unique Games Conjecture restricted to instance with a fairly mild condition on the expansion of small sets. Alongside, we obtain the first strong hardness of approximation results for the Balanced Separator and Minimum Linear Arrangement problems. Before, no such hardness was known for these problems even assuming the Unique Games Conjecture. #R##N#These results not only establish the Small-Set Expansion Hypothesis as a natural unifying hypothesis that implies the Unique Games Conjecture, all its consequences and, in addition, hardness results for other problems like Balanced Separator and Minimum Linear Arrangement, but our results also show that the Small-Set Expansion Hypothesis problem lies at the combinatorial heart of the Unique Games Conjecture. #R##N#The key technical ingredient is a new way of exploiting the structure of the Unique Games instances obtained from the Small-Set Expansion Hypothesis via (Raghavendra, Steurer, 2010). This additional structure allows us to modify standard reductions in a way that essentially destroys their local-gadget nature. Using this modification, we can argue about the expansion in the graphs produced by the reduction without relying on expansion properties of the underlying Unique Games instance (which would be impossible for a local-gadget reduction).

Connected through hyperedge HE-1 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering for euclidean data"
    - "hierarchical clustering with structural constraints"
    - "adaptive hierarchical clustering using ordinal queries"
    - "hierarchical clustering objective functions and algorithms"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "interactive bayesian hierarchical clustering"
    - "hierarchical clustering via spreading metrics"

Connected through hyperedge HE-2 (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"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "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"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-3 (induced by source paper "graph and string parameters connections between pathwidth cutwidth and the locality number"):
  with members:
    - "deterministic regular expressions with back references"
    - "rounding semidefinite programming hierarchies via global correlation"

Connected through hyperedge HE-4 (induced by source paper "losing treewidth by separating subsets"):
  with members:
    - "linear kernels for edge deletion problems to immersion closed graph classes"
    - "parameterized vertex deletion problems for hereditary graph classes with a block property"
    - "approximation and kernelization for chordal vertex deletion"
    - "a single exponential fixed parameter algorithm for distance hereditary vertex deletion"
    - "lp based robust algorithms for noisy minor free and bounded treewidth graphs"
    - "partitioning a graph into small pieces with applications to path transversal"
    - "on the computational complexity of vertex integrity and component order connectivity"
    - "polynomial bounds for the grid minor theorem"

Connected through hyperedge HE-5 (induced by source paper "finding cheeger cuts in hypergraphs via heat equation"):
  with members:
    - "polynomial time algorithms for submodular laplacian systems"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "cheeger inequalities for submodular transformations"
    - "diffusion operator and spectral analysis for directed hypergraph laplacian"
    - "heat kernel based community detection"

Connected through hyperedge HE-6 (induced by source paper "mildly exponential time approximation algorithms for vertex cover uniform sparsest cut and related problems"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"

Connected through hyperedge HE-7 (induced by source paper "chasing similarity distribution aware aggregation scheduling extended version"):
  with members:
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "bayesian locality sensitive hashing for fast similarity search"

Connected through hyperedge HE-8 (induced by source paper "effect of gromov hyperbolicity parameter on cuts and expansions in graphs and some algorithmic implications"):
  with members:
    - "the complexity of approximating vertex expansion"
    - "computing the gromov hyperbolicity of a discrete metric space"
    - "euclidean versus hyperbolic congestion in idealized versus experimental networks"

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)