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: "on the approximability of partial vc dimension"
  Abstract: We introduce the problem Partial VC Dimension that asks, given a hypergraph $H=(X,E)$ and integers $k$ and $\ell$, whether one can select a set $C\subseteq X$ of $k$ vertices of $H$ such that the set $\{e\cap C, e\in E\}$ of distinct hyperedge-intersections with $C$ has size at least $\ell$. The sets $e\cap C$ define equivalence classes over $E$. Partial VC Dimension is a generalization of VC Dimension, which corresponds to the case $\ell=2^k$, and of Distinguishing Transversal, which corresponds to the case $\ell=|E|$ (the latter is also known as Test Cover in the dual hypergraph). We also introduce the associated fixed-cardinality maximization problem Max Partial VC Dimension that aims at maximizing the number of equivalence classes induced by a solution set of $k$ vertices. We study the approximation complexity of Max Partial VC Dimension on general hypergraphs and on more restricted instances, in particular, neighborhood hypergraphs of graphs.

Connected through hyperedge HE-1 (induced by source paper "inapproximability of vc dimension and littlestone s dimension"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "detecting communities is hard and counting them is even harder"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "complexity theoretic limitations on learning halfspaces"
    - "eth hardness for signaling in symmetric zero sum games"
    - "hardness results for signaling in bayesian zero sum and network routing games"

Connected through hyperedge HE-2 (induced by source paper "parameterized and approximation complexity of partial vc dimension"):
  with members:
    - "inapproximability of vc dimension and littlestone s dimension"
    - "identification location domination and metric dimension on interval and permutation graphs i bounds"
    - "identification location domination and metric dimension on interval and permutation graphs ii algorithms and complexity"
    - "identifying codes in hereditary classes of graphs and vc dimension"
    - "hitting set for hypergraphs of low vc dimension"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "parameterizations of test cover with bounded test sizes"
    - "parameterized study of the test cover problem"

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)