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 gram schmidt walk a cure for the banaszczyk blues"
  Abstract: An important result in discrepancy due to Banaszczyk states that for any set of $n$ vectors in $\mathbb{R}^m$ of $\ell_2$ norm at most $1$ and any convex body $K$ in $\mathbb{R}^m$ of Gaussian measure at least half, there exists a $\pm 1$ combination of these vectors which lies in $5K$. This result implies the best known bounds for several problems in discrepancy. Banaszczyk's proof of this result is non-constructive and a major open problem has been to give an efficient algorithm to find such a $\pm 1$ combination of the vectors. #R##N#In this paper, we resolve this question and give an efficient randomized algorithm to find a $\pm 1$ combination of the vectors which lies in $cK$ for $c>0$ an absolute constant. This leads to new efficient algorithms for several problems in discrepancy theory.

Connected through hyperedge HE-1 (induced by source paper "a spectral approach to network design"):
  with members:
    - "network design for s t effective resistance"
    - "new notions and constructions of sparsification for graphs and hypergraphs"
    - "on a generalization of iterated and randomized rounding"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "graph clustering using effective resistance"
    - "algorithmic discrepancy beyond partial coloring"

Connected through hyperedge HE-2 (induced by source paper "generalised pattern matching revisited"):
  with members:
    - "constructive discrepancy minimization with hereditary l2 guarantees"
    - "algorithmic discrepancy beyond partial coloring"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-3 (induced by source paper "online vector balancing and geometric discrepancy"):
  with members:
    - "on line balancing of random inputs"
    - "online geometric discrepancy for stochastic arrivals with applications to envy minimization"
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "efficient algorithms for discrepancy minimization in convex sets"
    - "constructive discrepancy minimization for convex sets"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-4 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization"):
  with members:
    - "on line balancing of random inputs"
    - "on the discrepancy of random low degree set systems"
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "efficient algorithms for discrepancy minimization in convex sets"
    - "fair assignment of indivisible objects under ordinal preferences"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-5 (induced by source paper "linear size sparsifier and the geometry of the operator norm ball"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "constructive discrepancy minimization for convex sets"
    - "a matrix hyperbolic cosine algorithm and applications"
    - "constructive algorithms for discrepancy minimization"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-6 (induced by source paper "spherical discrepancy minimization and algorithmic lower bounds for covering the sphere"):
  with members:
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "constructive discrepancy minimization by walking on the edges"
    - "spherical coverage verification"

Connected through hyperedge HE-7 (induced by source paper "sticky brownian rounding and its applications to constraint satisfaction problems"):
  with members:
    - "krivine diffusions attain the goemans williamson approximation ratio"
    - "constructive discrepancy minimization by walking on the edges"
    - "better balance by being biased a 0 8776 approximation for max bisection"
    - "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-8 (induced by source paper "near optimal coresets of kernel density estimates"):
  with members:
    - "improved coresets for kernel density estimates"
    - "geometric inference on kernel density estimates"
    - "on the equivalence between herding and conditional gradient algorithms"
    - "a gentle introduction to the kernel distance"
    - "algorithms for eps approximations of terrains"

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)