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: "taming the curse of dimensionality discrete integration by hashing and optimization"
  Abstract: Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives a constant-factor approximation of a general discrete integral defined over an exponentially large set. This algorithm relies on solving only a small number of instances of a discrete combinatorial optimization problem subject to randomly generated parity constraints used as a hash function. As an application, we demonstrate that with a small number of MAP queries we can efficiently approximate the partition function of discrete graphical models, which can in turn be used, for instance, for marginal computation or model selection.

Connected through hyperedge HE-1 (induced by source paper "towards efficient discrete integration via adaptive quantile queries"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "stable model counting and its application in probabilistic logic programming"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-2 (induced by source paper "applications of nature inspired algorithms for dimension reduction enabling efficient data analytics"):
  with members:
    - "evolutionary computation optimization and learning algorithms for data science"
    - "cascaded recurrent neural networks for hyperspectral image classification"

Connected through hyperedge HE-3 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-4 (induced by source paper "learning to reason leveraging neural networks for approximate dnf counting"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "how powerful are graph neural networks"
    - "inference in probabilistic graphical models by graph neural networks"
    - "on hashing based approaches to approximate dnf counting"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "phase transition behavior of cardinality and xor constraints"):
  with members:
    - "combining the k cnf and xor phase transitions"
    - "the hard problems are almost everywhere for random cnf xor formulas"
    - "property driven fence insertion using reorder bounded model checking"
    - "distribution aware sampling and weighted model counting for sat"
    - "a scalable approximate model counter"
    - "list decoding of polar codes"
    - "probabilistic planning via heuristic forward search and weighted model counting"

Connected through hyperedge HE-6 (induced by source paper "adawish faster discrete integration via adaptive quantiles"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "closing the gap between short and long xors for model counting"
    - "stable model counting and its application in probabilistic logic programming"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-7 (induced by source paper "empirical bounds on linear regions of deep rectifier networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "on decision regions of narrow deep neural networks"
    - "tropical geometry of deep neural networks"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "provable robustness of relu networks via maximization of linear regions"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"

Connected through hyperedge HE-8 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice"):
  with members:
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "structured learning modulo theories"
    - "a scalable approximate model counter"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "automated feedback generation for introductory programming assignments"

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.LG(Machine Learning)