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: "approximate hamming distance in a stream"
  Abstract: We consider the problem of computing a $(1+\epsilon)$-approximation of the Hamming distance between a pattern of length $n$ and successive substrings of a stream. We first look at the one-way randomised communication complexity of this problem, giving Alice the first half of the stream and Bob the second half. We show the following: (1) If Alice and Bob both share the pattern then there is an $O(\epsilon^{-4} \log^2 n)$ bit randomised one-way communication protocol. (2) If only Alice has the pattern then there is an $O(\epsilon^{-2}\sqrt{n}\log n)$ bit randomised one-way communication protocol. #R##N#We then go on to develop small space streaming algorithms for $(1+\epsilon)$-approximate Hamming distance which give worst case running time guarantees per arriving symbol. (1) For binary input alphabets there is an $O(\epsilon^{-3} \sqrt{n} \log^{2} n)$ space and $O(\epsilon^{-2} \log{n})$ time streaming $(1+\epsilon)$-approximate Hamming distance algorithm. (2) For general input alphabets there is an $O(\epsilon^{-5} \sqrt{n} \log^{4} n)$ space and $O(\epsilon^{-4} \log^3 {n})$ time streaming $(1+\epsilon)$-approximate Hamming distance algorithm.

Connected through hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances"):
  with members:
    - "l p pattern matching in a stream"
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "the streaming k mismatch problem"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-2 (induced by source paper "approximating text to pattern distance via dimensionality reduction"):
  with members:
    - "l p pattern matching in a stream"
    - "approximating approximate pattern matching"
    - "the streaming k mismatch problem"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-3 (induced by source paper "sliding window property testing for regular languages"):
  with members:
    - "visibly pushdown languages over sliding windows"
    - "randomized sliding window algorithms for regular languages"
    - "automata theory on sliding windows"
    - "streaming pattern matching with d wildcards"
    - "dictionary matching in a stream"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"

Connected through hyperedge HE-4 (induced by source paper "l p pattern matching in a stream"):
  with members:
    - "approximating approximate pattern matching"
    - "the streaming k mismatch problem"
    - "the k mismatch problem revisited"
    - "how to catch l 2 heavy hitters on sliding windows"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-5 (induced by source paper "the streaming k mismatch problem"):
  with members:
    - "optimal trade offs for pattern matching with k mismatches"
    - "streaming periodicity with mismatches"
    - "streaming k mismatch with error correcting and applications"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"
    - "parameterized matching in the streaming model"

Connected through hyperedge HE-6 (induced by source paper "automata theory on sliding windows"):
  with members:
    - "dictionary matching in a stream"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"
    - "validating xml documents in the streaming model with external memory"

Connected through hyperedge HE-7 (induced by source paper "streaming k mismatch with error correcting and applications"):
  with members:
    - "the streaming k mismatch problem"
    - "indexing weighted sequences neat and efficient"
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "efficient index for weighted sequences"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "probabilistic threshold indexing for uncertain strings"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-8 (induced by source paper "streaming k mismatch with data recovery and applications"):
  with members:
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "probabilistic threshold indexing for uncertain strings"
    - "space lower bounds for online pattern matching"

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)