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: "constructive discrepancy minimization for convex sets"
  Abstract: A classical theorem of Spencer shows that any set system with n sets and n elements admits a coloring of discrepancy O(n^1/2). Recent exciting work of Bansal, Lovett and Meka shows that such colorings can be found in polynomial time. In fact, the Lovett-Meka algorithm finds a half integral point in any "large enough" polytope. However, their algorithm crucially relies on the facet structure and does not apply to general convex sets. #R##N#We show that for any symmetric convex set K with measure at least exp(-n/500), the following algorithm finds a point y in K \cap [-1,1]^n with Omega(n) coordinates in {-1,+1}: (1) take a random Gaussian vector x; (2) compute the point y in K \cap [-1,1]^n that is closest to x. (3) return y. #R##N#This provides another truly constructive proof of Spencer's theorem and the first constructive proof of a Theorem of Gluskin and Giannopoulos.

Connected through hyperedge HE-1 (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"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "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 by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-2 (induced by source paper "balancing gaussian vectors in high dimension"):
  with members:
    - "on the discrepancy of random low degree set systems"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "constructive algorithms for discrepancy minimization"
    - "analysis of the karmarkar karp differencing algorithm"

Connected through hyperedge HE-3 (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"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "a matrix hyperbolic cosine algorithm and applications"
    - "constructive algorithms for discrepancy minimization"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-4 (induced by source paper "algorithms to approximate column sparse packing problems"):
  with members:
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "random order contention resolution schemes"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "improved algorithmic bounds for discrepancy of sparse set systems"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "non negative submodular stochastic probing via stochastic contention resolution schemes"

Connected through hyperedge HE-5 (induced by source paper "the gram schmidt walk a cure for the banaszczyk blues"):
  with members:
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "approximation friendly discrepancy rounding"
    - "approximating bin packing within o log opt log log opt bins"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-6 (induced by source paper "algorithmic discrepancy beyond partial coloring"):
  with members:
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "approximation friendly discrepancy rounding"
    - "approximating bin packing within o log opt log log opt bins"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-7 (induced by source paper "an algorithm for koml os conjecture matching banaszczyk s bound"):
  with members:
    - "improved algorithmic bounds for discrepancy of sparse set systems"
    - "approximating bin packing within o log opt log log opt bins"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-8 (induced by source paper "improved algorithmic bounds for discrepancy of sparse set systems"):
  with members:
    - "approximating bin packing within o log opt log log opt bins"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

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.DM(Discrete Mathematics)