human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lower bounds for text indexing with mismatches and differences"):
  Abstract: In this paper we study lower bounds for the fundamental problem of text indexing with mismatches and differences. In this problem we are given a long string of length $n$, the "text", and the task is to preprocess it into a data structure such that given a query string $Q$, one can quickly identify substrings that are within Hamming or edit distance at most $k$ from $Q$. This problem is at the core of various problems arising in biology and text processing. While exact text indexing allows linear-size data structures with linear query time, text indexing with $k$ mismatches (or $k$ differences) seems to be much harder: All known data structures have exponential dependency on $k$ either in the space, or in the time bound. We provide conditional and pointer-machine lower bounds that make a step toward explaining this phenomenon. We start by demonstrating lower bounds for $k = \Theta(\log n)$. We show that assuming the Strong Exponential Time Hypothesis, any data structure for text indexing that can be constructed in polynomial time cannot have $\mathcal{O}(n^{1-\delta})$ query time, for any $\delta>0$. This bound also extends to the setting where we only ask for $(1+\varepsilon)$-approximate solutions for text indexing. However, in many applications the value of $k$ is rather small, and one might hope that for small~$k$ we can develop more efficient solutions. We show that this would require a radically new approach as using the current methods one cannot avoid exponential dependency on $k$ either in the space, or in the time bound for all even $\frac{8}{\sqrt{3}} \sqrt{\log n} \le k = o(\log n)$. Our lower bounds also apply to the dictionary look-up problem, where instead of a text one is given a set of strings.

Includes members:
  Member "approximate nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "hardness of approximate nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "data structure lower bounds for document indexing problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast join project query evaluation using matrix multiplication")
  Member "probabilistic polynomials and hamming nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithms for similarity search and pseudorandomness")

Question: Please tell me which class the source paper 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)