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: "tight tradeoffs for real time approximation of longest palindromes in streams"
  Abstract: We consider computing a longest palindrome in the streaming model, where the symbols arrive one-by-one and we do not have random access to the input. While computing the answer exactly using sublinear space is not possible in such a setting, one can still hope for a good approximation guarantee. Our contribution is twofold. First, we provide lower bounds on the space requirements for randomized approximation algorithms processing inputs of length $n$. We rule out Las Vegas algorithms, as they cannot achieve sublinear space complexity. For Monte Carlo algorithms, we prove a lower bounds of $\Omega( M \log\min\{|\Sigma|,M\})$ bits of memory; here $M=n/E$ for approximating the answer with additive error $E$, and $M= \frac{\log n}{\log (1+\varepsilon)}$ for approximating the answer with multiplicative error $(1 + \varepsilon)$. Second, we design three real-time algorithms for this problem. Our Monte Carlo approximation algorithms for both additive and multiplicative versions of the problem use $O(M)$ words of memory. Thus the obtained lower bounds are asymptotically tight up to a logarithmic factor. The third algorithm is deterministic and finds a longest palindrome exactly if it is short. This algorithm can be run in parallel with a Monte Carlo algorithm to obtain better results in practice. Overall, both the time and space complexity of finding a longest palindrome in a stream are essentially settled.

Connected through hyperedge HE-1 (induced by source paper "periodicity in data streams with wildcards"):
  with members:
    - "the streaming k mismatch problem"
    - "streaming periodicity with mismatches"
    - "streaming pattern matching with d wildcards"
    - "streaming k mismatch with error correcting and applications"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "space efficient string indexing for wildcard pattern matching"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-2 (induced by source paper "longest alignment with edits in data streams"):
  with members:
    - "streaming for aibohphobes longest palindrome with mismatches"
    - "streaming algorithms for computing edit distance without exploiting suffix trees"
    - "edit distance sketching streaming and document exchange"
    - "longest common substrings with k mismatches"
    - "the k mismatch problem revisited"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "sublinear space algorithms for the longest common substring problem"
    - "palindrome recognition in the streaming model"

Connected through hyperedge HE-3 (induced by source paper "streaming periodicity with mismatches"):
  with members:
    - "streaming pattern matching with d wildcards"
    - "streaming k mismatch with data recovery and applications"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-4 (induced by source paper "streaming for aibohphobes longest palindrome with mismatches"):
  with members:
    - "edit distance sketching streaming and document exchange"
    - "the k mismatch problem revisited"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-5 (induced by source paper "diverse palindromic factorization is np complete"):
  with members:
    - "a subquadratic algorithm for minimum palindromic factorization"

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)