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: "better balance by being biased a 0 8776 approximation for max bisection"
  Abstract: Recently Raghavendra and Tan (SODA 2012) gave a 0.85-approximation algorithm for the Max Bisection problem. We improve their algorithm to a 0.8776-approximation. As Max Bisection is hard to approximate within $\alpha_{GW} + \epsilon \approx 0.8786$ under the Unique Games Conjecture (UGC), our algorithm is nearly optimal. We conjecture that Max Bisection is approximable within $\alpha_{GW}-\epsilon$, i.e., the bisection constraint (essentially) does not make Max Cut harder. #R##N#We also obtain an optimal algorithm (assuming the UGC) for the analogous variant of Max 2-Sat. Our approximation ratio for this problem exactly matches the optimal approximation ratio for Max 2-Sat, i.e., $\alpha_{LLZ} + \epsilon \approx 0.9401$, showing that the bisection constraint does not make Max 2-Sat harder. This improves on a 0.93-approximation for this problem due to Raghavendra and Tan.

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 "global cardinality constraints make approximating some max 2 csps harder"):
  with members:
    - "resolution lower bounds for refutation statements"
    - "a note on max k vertex cover faster fpt as smaller approximate kernel and improved approximation"
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "analysis of boolean functions"
    - "approximating csps with global cardinality constraints using sdp hierarchies"
    - "the complexity of global cardinality constraints"
    - "conditional hardness for approximate coloring"

Connected through hyperedge HE-3 (induced by source paper "sticky brownian rounding and its applications to constraint satisfaction problems"):
  with members:
    - "krivine diffusions attain the goemans williamson approximation ratio"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "constructive discrepancy minimization by walking on the edges"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating csps with global cardinality constraints using sdp hierarchies"
    - "constructive algorithms for discrepancy minimization"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "a note on max k vertex cover faster fpt as smaller approximate kernel and improved approximation"):
  with members:
    - "an fpt algorithm beating 2 approximation for k cut"
    - "reductions between expansion problems"

Connected through hyperedge HE-5 (induced by source paper "integrality gaps and approximation algorithms for dispersers and bipartite expanders"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"
    - "constant factor lasserre integrality gaps for graph partitioning problems"
    - "approximation algorithm for non boolean max k csp"
    - "explicit optimal hardness via gaussian stability results"
    - "min max graph partitioning and small set expansion"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"
    - "approximating csps with global cardinality constraints using sdp hierarchies"

Connected through hyperedge HE-6 (induced by source paper "parameterized algorithms for constraint satisfaction problems above average with global cardinality constraints"):
  with members:
    - "analysis of boolean functions"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximating csps with global cardinality constraints using sdp hierarchies"
    - "note on maximal bisection above tight lower bound"
    - "reductions between expansion problems"
    - "a probabilistic approach to problems parameterized above or below tight bounds"
    - "solving max r sat above a tight lower bound"
    - "approximation resistant predicates from pairwise independence"

Connected through hyperedge HE-7 (induced by source paper "conditioning and covariance on caterpillars"):
  with members:
    - "rounding sum of squares relaxations"
    - "hypercontractivity sum of squares proofs and their applications"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating csps with global cardinality constraints using sdp hierarchies"

Connected through hyperedge HE-8 (induced by source paper "comparing apples and oranges query tradeoff in submodular maximization"):
  with members:
    - "near optimal nonmyopic value of information in graphical models"
    - "symmetry and approximability of submodular maximization problems"
    - "learning with submodular functions a convex optimization perspective"

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)