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: "approximating constraint satisfaction problems on high dimensional expanders"
  Abstract: We consider the problem of approximately solving constraint satisfaction problems with arity $k > 2$ ($k$-CSPs) on instances satisfying certain expansion properties, when viewed as hypergraphs. Random instances of $k$-CSPs, which are also highly expanding, are well-known to be hard to approximate using known algorithmic techniques (and are widely believed to be hard to approximate in polynomial time). However, we show that this is not necessarily the case for instances where the hypergraph is a high-dimensional expander. #R##N#We consider the spectral definition of high-dimensional expansion used by Dinur and Kaufman [FOCS 2017] to construct certain primitives related to PCPs. They measure the expansion in terms of a parameter $\gamma$ which is the analogue of the second singular value for expanding graphs. Extending the results by Barak, Raghavendra and Steurer [FOCS 2011] for 2-CSPs, we show that if an instance of MAX k-CSP over alphabet $[q]$ is a high-dimensional expander with parameter $\gamma$, then it is possible to approximate the maximum fraction of satisfiable constraints up to an additive error $\epsilon$ using $q^{O(k)} \cdot (k/\epsilon)^{O(1)}$ levels of the sum-of-squares SDP hierarchy, provided $\gamma \leq \epsilon^{O(1)} \cdot (1/(kq))^{O(k)}$. #R##N#Based on our analysis, we also suggest a notion of threshold-rank for hypergraphs, which can be used to extend the results for approximating 2-CSPs on low threshold-rank graphs. We show that if an instance of MAX k-CSP has threshold rank $r$ for a threshold $\tau = (\epsilon/k)^{O(1)} \cdot (1/q)^{O(k)}$, then it is possible to approximately solve the instance up to additive error $\epsilon$, using $r \cdot q^{O(k)} \cdot (k/\epsilon)^{O(1)}$ levels of the sum-of-squares hierarchy. As in the case of graphs, high-dimensional expanders (with sufficiently small $\gamma$) have threshold rank 1 according to our definition.

Connected through hyperedge HE-1 (induced by source paper "improved analysis of higher order random walks and applications"):
  with members:
    - "high dimensional expanders from expanders"
    - "agreement testing theorems on layered set systems"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "walking on the edge and cosystolic expansion"
    - "high dimensional random walks and colorful expansion"
    - "high dimensional expanders and property testing"

Connected through hyperedge HE-2 (induced by source paper "agreement testing theorems on layered set systems"):
  with members:
    - "small set expansion in shortcode graph and the 2 to 2 conjecture"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "cube vs cube low degree test"
    - "high dimensional random walks and colorful expansion"
    - "high dimensional expanders and property testing"

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)