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: "subsampling mathematical relaxations and average case complexity"
  Abstract: We initiate a study of when the value of mathematical relaxations such as linear and semidefinite programs for constraint satisfaction problems (CSPs) is approximately preserved when restricting the instance to a sub-instance induced by a small random subsample of the variables. Let $C$ be a family of CSPs such as 3SAT, Max-Cut, etc., and let $\Pi$ be a relaxation for $C$, in the sense that for every instance $P\in C$, $\Pi(P)$ is an upper bound the maximum fraction of satisfiable constraints of $P$. Loosely speaking, we say that subsampling holds for $C$ and $\Pi$ if for every sufficiently dense instance $P \in C$ and every $\epsilon>0$, if we let $P'$ be the instance obtained by restricting $P$ to a sufficiently large constant number of variables, then $\Pi(P') \in (1\pm \epsilon)\Pi(P)$. We say that weak subsampling holds if the above guarantee is replaced with $\Pi(P')=1-\Theta(\gamma)$ whenever $\Pi(P)=1-\gamma$. We show: 1. Subsampling holds for the BasicLP and BasicSDP programs. BasicSDP is a variant of the relaxation considered by Raghavendra (2008), who showed it gives an optimal approximation factor for every CSP under the unique games conjecture. BasicLP is the linear programming analog of BasicSDP. 2. For tighter versions of BasicSDP obtained by adding additional constraints from the Lasserre hierarchy, weak subsampling holds for CSPs of unique games type. 3. There are non-unique CSPs for which even weak subsampling fails for the above tighter semidefinite programs. Also there are unique CSPs for which subsampling fails for the Sherali-Adams linear programming hierarchy. As a corollary of our weak subsampling for strong semidefinite programs, we obtain a polynomial-time algorithm to certify that random geometric graphs (of the type considered by Feige and Schechtman, 2002) of max-cut value $1-\gamma$ have a cut value at most $1-\gamma/10$.

Connected through hyperedge HE-1 (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  with members:
    - "correlation clustering in data streams"
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "sketching cuts in graphs and hypergraphs"
    - "the sketching complexity of graph cuts"
    - "streaming lower bounds for approximating max cut"
    - "better bounds for matchings in the streaming model"
    - "a note on no need to choose how to get both a ptas and sublinear query complexity"
    - "single pass sparsification in the streaming model with edge deletions"

Connected through hyperedge HE-2 (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"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-3 (induced by source paper "a birthday repetition theorem and complexity of approximating dense csps"):
  with members:
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "on the hardness of signaling"
    - "am with multiple merlins"
    - "label cover instances with large girth and the hardness of approximating basic k spanner"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximation algorithms and hardness of the k route cut problem"

Connected through hyperedge HE-4 (induced by source paper "on percolation and np hardness"):
  with members:
    - "on independent sets in random graphs"
    - "improved hardness of approximating chromatic number"
    - "recoverable values for independent sets"
    - "computational transition at the uniqueness threshold"
    - "conditional hardness for approximate coloring"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-5 (induced by source paper "hierarchies of relaxations for online prediction problems with evolving constraints"):
  with members:
    - "tensor prediction rademacher complexity and random 3 xor"
    - "online local learning via semidefinite programming"
    - "rounding lasserre sdps using column selection and spectrum based approximation schemes for graph partitioning and quadratic ips"
    - "near optimal algorithms for online matrix prediction"

Connected through hyperedge HE-6 (induced by source paper "amplification and derandomization without slowdown"):
  with members:
    - "approximating dense max 2 csps"
    - "am with multiple merlins"

Connected through hyperedge HE-7 (induced by source paper "approximating dense max 2 csps"):
  with members:
    - "improved approximation algorithms for projection games"
    - "am with multiple merlins"

Connected through hyperedge HE-8 (induced by source paper "lower bound for the unique games problem"):
  with members:
    - "spectral algorithms for unique games"

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.CC(Computational Complexity)