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: "optimization with parity constraints from binary codes to discrete integration"
  Abstract: Many probabilistic inference tasks involve summations over exponentially large sets. Recently, it has been shown that these problems can be reduced to solving a polynomial number of MAP inference queries for a model augmented with randomly generated parity constraints. By exploiting a connection with max-likelihood decoding of binary codes, we show that these optimizations are computationally hard. Inspired by iterative message passing decoding algorithms, we propose an Integer Linear Programming (ILP) formulation for the problem, enhanced with new sparsification techniques to improve decoding performance. By solving the ILP through a sequence of LP relaxations, we get both lower and upper bounds on the partition function, which hold with high probability and are much tighter than those obtained with variational methods.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "automated feedback generation for introductory programming assignments"

Connected through hyperedge HE-4 (induced by source paper "approximate inference via weighted rademacher complexity"):
  with members:
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "high dimensional inference with random maximum a posteriori perturbations"
    - "provable submodular minimization using wolfe s algorithm"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "on the partition function and random maximum a posteriori perturbations"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-5 (induced by source paper "high dimensional discrete integration over the hypergrid"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "approximating the partition function of the ferromagnetic potts model"
    - "an approximation algorithm for k sat"
    - "the good old davis putnam procedure helps counting models"

Connected through hyperedge HE-6 (induced by source paper "high dimensional discrete integration by hashing and optimization"):
  with members:
    - "an approximation algorithm for k sat"

Connected through hyperedge HE-7 (induced by source paper "high dimensional inference with random maximum a posteriori perturbations"):
  with members:
    - "direct optimization through arg max for discrete variational auto encoder"
    - "approximate inference via weighted rademacher complexity"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "on measure concentration of random maximum a posteriori perturbations"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-8 (induced by source paper "solving marginal map problems with np oracles and parity constraints"):
  with members:
    - "decomposition bounds for marginal map"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "maximizing the spread of cascades using network design"

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.AI(Artificial Intelligence)