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: "a polynomial time algorithm to approximate the mixed volume within a simply exponential factor"
  Abstract: Let ${\bf K} = (K_1, ..., K_n)$ be an $n$-tuple of convex compact subsets in the Euclidean space $\R^n$, and let $V(\cdot)$ be the Euclidean volume in $\R^n$. The Minkowski polynomial $V_{\bf K}$ is defined as $V_{\bf K}(\lambda_1, ... ,\lambda_n) = V(\lambda_1 K_1 +, ..., + \lambda_n K_n)$ and the mixed volume $V(K_1, ..., K_n)$ as $$ V(K_1, ..., K_n) = \frac{\partial^n}{\partial \lambda_1...\partial \lambda_n} V_{\bf K}(\lambda_1 K_1 +, ..., + \lambda_n K_n). $$ Our main result is a poly-time algorithm which approximates $V(K_1, ..., K_n)$ with multiplicative error $e^n$ and with better rates if the affine dimensions of most of the sets $K_i$ are small. Our approach is based on a particular approximation of $\log(V(K_1, ..., K_n))$ by a solution of some convex minimization problem. We prove the mixed volume analogues of the Van der Waerden and Schrijver-Valiant conjectures on the permanent. These results, interesting on their own, allow us to justify the abovementioned approximation by a convex minimization, which is solved using the ellipsoid method and a randomized poly-time time algorithm for the approximation of the volume of a convex set.

Connected through hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale"):
  with members:
    - "flexible modeling of diversity with strongly log concave distributions"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "strong log concavity does not imply log submodularity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "data summarization at scale a two stage submodular approach"
    - "a tight approximation for submodular maximization with mixed packing and covering constraints"

Connected through hyperedge HE-2 (induced by source paper "gauges loops and polynomials for partition functions of graphical models"):
  with members:
    - "the ising partition function zeros and deterministic approximation"
    - "belief propagation bethe approximation and polynomials"
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "how to calculate partition functions using convex programming hierarchies provable bounds for variational methods"
    - "a faster approximation algorithm for the gibbs partition function"
    - "the bethe permanent of a nonnegative matrix"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "a cluster cumulant expansion at the fixed points of belief propagation"

Connected through hyperedge HE-3 (induced by source paper "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"):
  with members:
    - "approximately counting bases of bicircular matroids"
    - "log concave polynomials i entropy and a deterministic approximation algorithm for counting bases of matroids"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "high dimensional random walks and colorful expansion"
    - "random cluster dynamics for the ising model is rapidly mixing"
    - "approximating the tutte polynomial of a binary matroid and other related combinatorial polynomials"
    - "the complexity of computing the sign of the tutte polynomial"
    - "inapproximability of the tutte polynomial of a planar graph"

Connected through hyperedge HE-4 (induced by source paper "log concave polynomials i entropy and a deterministic approximation algorithm for counting bases of matroids"):
  with members:
    - "nash social welfare for indivisible items under separable piecewise linear concave utilities"
    - "real stable polynomials and matroids optimization and counting"
    - "entropy optimization and counting"

Connected through hyperedge HE-5 (induced by source paper "a proof of the log concavity conjecture related to the computation of the ergodic capacity of mimo channels"):
  with members:
    - "statistical eigenmode transmission over jointly correlated mimo channels"

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.CG(Computational Geometry)