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 linear time algorithm for trust region problems"
  Abstract: We consider the fundamental problem of maximizing a general quadratic function over an ellipsoidal domain, also known as the trust region problem. We give the first provable linear-time (in the number of non-zero entries of the input) algorithm for approximately solving this problem. Specifically, our algorithm returns an $\epsilon$-approximate solution in time $\tilde{O}(N/\sqrt{\epsilon})$, where $N$ is the number of non-zero entries in the input. This matches the runtime of Nesterov's accelerated gradient descent, suitable for the special case in which the quadratic function is concave, and the runtime of the Lanczos method which is applicable when the problem is purely quadratic.

Connected through hyperedge HE-1 (induced by source paper "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "learning in non convex games with an optimization oracle"
    - "how to use heuristics for differential privacy"
    - "calibrating noise to variance in adaptive data analysis"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-2 (induced by source paper "a unified framework for data poisoning attack to graph based semi supervised learning"):
  with members:
    - "adversarial attack on graph structured data"
    - "implicit reparameterization gradients"
    - "attack graph convolutional networks by adding fake nodes"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "data poisoning attacks against online learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-3 (induced by source paper "a dual approach to scalable verification of deep networks"):
  with members:
    - "adversarial vulnerability for any classifier"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-5 (induced by source paper "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "non convex phase retrieval from stft measurements"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "fast algorithms for robust pca via gradient descent"
    - "phase retrieval using gauss newton method"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-6 (induced by source paper "sub sampled cubic regularization for non convex optimization"):
  with members:
    - "starting small learning with adaptive sample sizes"
    - "variance reduced stochastic gradient descent with neighbors"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "most tensor problems are np hard"

Connected through hyperedge HE-7 (induced by source paper "complete dictionary recovery over the sphere"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "guaranteed matrix completion via non convex factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "on the computational efficiency of training neural networks"

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)