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: "strongly refuting random csps below the spectral threshold"
  Abstract: Random constraint satisfaction problems (CSPs) are known to exhibit threshold phenomena: given a uniformly random instance of a CSP with $n$ variables and $m$ clauses, there is a value of $m = \Omega(n)$ beyond which the CSP will be unsatisfiable with high probability. Strong refutation is the problem of certifying that no variable assignment satisfies more than a constant fraction of clauses; this is the natural algorithmic problem in the unsatisfiable regime (when $m/n = \omega(1)$). #R##N#Intuitively, strong refutation should become easier as the clause density $m/n$ grows, because the contradictions introduced by the random clauses become more locally apparent. For CSPs such as $k$-SAT and $k$-XOR, there is a long-standing gap between the clause density at which efficient strong refutation algorithms are known, $m/n \ge \widetilde O(n^{k/2-1})$, and the clause density at which instances become unsatisfiable with high probability, $m/n = \omega (1)$. #R##N#In this paper, we give spectral and sum-of-squares algorithms for strongly refuting random $k$-XOR instances with clause density $m/n \ge \widetilde O(n^{(k/2-1)(1-\delta)})$ in time $\exp(\widetilde O(n^{\delta}))$ or in $\widetilde O(n^{\delta})$ rounds of the sum-of-squares hierarchy, for any $\delta \in [0,1)$ and any integer $k \ge 3$. Our algorithms provide a smooth transition between the clause density at which polynomial-time algorithms are known at $\delta = 0$, and brute-force refutation at the satisfiability threshold when $\delta = 1$. We also leverage our $k$-XOR results to obtain strong refutation algorithms for SAT (or any other Boolean CSP) at similar clause densities. Our algorithms match the known sum-of-squares lower bounds due to Grigoriev and Schonebeck, up to logarithmic factors. #R##N#Additionally, we extend our techniques to give new results for certifying upper bounds on the injective tensor norm of random tensors.

Connected through hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "communication and memory efficient testing of discrete distributions"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "mixing complexity and its applications to neural networks"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretically secure databases"
    - "how to refute a random csp"

Connected through hyperedge HE-2 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "exact tensor completion with sum of squares"
    - "sum of squares lower bounds for refuting any csp"
    - "spectral algorithms for tensor completion"
    - "complexity theoretic limitations on learning halfspaces"
    - "learning without concentration"
    - "nuclear norm of higher order tensors"
    - "a new convex relaxation for tensor completion"

Connected through hyperedge HE-3 (induced by source paper "the kikuchi hierarchy and tensor pca"):
  with members:
    - "the power of sum of squares for detecting hidden structures"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "sum of squares certificates for maxima of random tensors on the sphere"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "how to refute a random csp"
    - "asymptotic mutual information for the two groups stochastic block model"

Connected through hyperedge HE-4 (induced by source paper "extended formulation lower bounds for refuting random csps"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "the sdp value for random two eigenvalue csps"):
  with members:
    - "the threshold for sdp refutation of random regular nae 3sat"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "sum of squares lower bounds for refuting any csp"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "a composition theorem for parity kill number"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-6 (induced by source paper "lifting sum of squares lower bounds degree 2 to degree 4"):
  with members:
    - "extended formulation lower bounds for refuting random csps"
    - "the threshold for sdp refutation of random regular nae 3sat"
    - "the power of sum of squares for detecting hidden structures"
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "semidefinite programs on sparse random graphs and their application to community detection"

Connected through hyperedge HE-7 (induced by source paper "sum of squares meets nash optimal lower bounds for finding any equilibrium"):
  with members:
    - "better agnostic clustering via relaxed tensor norms"
    - "sum of squares lower bounds for refuting any csp"
    - "how to refute a random csp"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "settling the complexity of computing two player nash equilibria"
    - "games of fixed rank a hierarchy of bimatrix games"

Connected through hyperedge HE-8 (induced by source paper "sherali adams strikes back"):
  with members:
    - "high dimensional estimation via sum of squares proofs"
    - "the power of sum of squares for detecting hidden structures"
    - "how to refute a random csp"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "subsampling mathematical relaxations and average case complexity"
    - "max cut and the smallest eigenvalue"

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)