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: "sum of squares proofs and the quest toward optimal algorithms"
  Abstract: In order to obtain the best-known guarantees, algorithms are traditionally tailored to the particular problem we want to solve. Two recent developments, the Unique Games Conjecture (UGC) and the Sum-of-Squares (SOS) method, surprisingly suggest that this tailoring is not necessary and that a single efficient algorithm could achieve best possible guarantees for a wide range of different problems. #R##N#The Unique Games Conjecture (UGC) is a tantalizing conjecture in computational complexity, which, if true, will shed light on the complexity of a great many problems. In particular this conjecture predicts that a single concrete algorithm provides optimal guarantees among all efficient algorithms for a large class of computational problems. #R##N#The Sum-of-Squares (SOS) method is a general approach for solving systems of polynomial constraints. This approach is studied in several scientific disciplines, including real algebraic geometry, proof complexity, control theory, and mathematical programming, and has found applications in fields as diverse as quantum information theory, formal verification, game theory and many others. #R##N#We survey some connections that were recently uncovered between the Unique Games Conjecture and the Sum-of-Squares method. In particular, we discuss new tools to rigorously bound the running time of the SOS method for obtaining approximate solutions to hard optimization problems, and how these tools give the potential for the sum-of-squares method to provide new guarantees for many problems of interest, and possibly to even refute the UGC.

Connected through hyperedge HE-1 (induced by source paper "sum of squares bounds for the ordering principle"):
  with members:
    - "sum of squares lower bounds from symmetry and a good story"
    - "hypercontractivity sum of squares proofs and their applications"

Connected through hyperedge HE-2 (induced by source paper "efficiently learning structured distributions from untrusted batches"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "high dimensional estimation via sum of squares proofs"
    - "federated learning strategies for improving communication efficiency"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning discrete distributions from untrusted batches"
    - "learning populations of parameters"

Connected through hyperedge HE-3 (induced by source paper "new dependencies of hierarchies in polynomial optimization"):
  with members:
    - "optimization over the boolean hypercube via sums of nonnegative circuit polynomials"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "the power of sherali adams relaxations for general valued csps"
    - "a 1 epsilon approximation for makespan scheduling with precedence constraints using lp hierarchies"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "learning dynamic polynomial proofs"):
  with members:
    - "holist an environment for machine learning of higher order theorem proving"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "gamepad a learning environment for theorem proving"
    - "reinforcement learning of theorem proving"
    - "playing atari with deep reinforcement learning"
    - "control design along trajectories with sums of squares programming"

Connected through hyperedge HE-5 (induced by source paper "a tight degree 4 sum of squares lower bound for the sherrington kirkpatrick hamiltonian"):
  with members:
    - "computational hardness of certifying bounds on constrained pca problems"
    - "bayesian estimation from few samples community detection and related problems"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-6 (induced by source paper "on the connection between learning two layers neural networks and tensor decomposition"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the power of sum of squares for detecting hidden structures"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "polynomial time tensor decompositions with sum of squares"

Connected through hyperedge HE-7 (induced by source paper "optimization over the boolean hypercube via sums of nonnegative circuit polynomials"):
  with members:
    - "on the bit complexity of sum of squares proofs"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on the hardest problem formulations for the 0 1 lasserre hierarchy"
    - "sum of squares lower bounds for planted clique"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "sum of squares hierarchy lower bounds for symmetric formulations"
    - "approximating csps with global cardinality constraints using sdp hierarchies"

Connected through hyperedge HE-8 (induced by source paper "mildly exponential time approximation algorithms for vertex cover uniform sparsest cut and related problems"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "reductions between expansion problems"

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)