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: "straggler identification in round trip data streams via newton s identities and invertible bloom filters"
  Abstract: We introduce the straggler identification problem, in which an algorithm must determine the identities of the remaining members of a set after it has had a large number of insertion and deletion operations performed on it, and now has relatively few remaining members. The goal is to do this in o(n) space, where n is the total number of identities. The straggler identification problem has applications, for example, in determining the set of unacknowledged packets in a high-bandwidth multicast data stream. We provide a deterministic solution to the straggler identification problem that uses only O(d log n) bits and is based on a novel application of Newton's identities for symmetric polynomials. This solution can identify any subset of d stragglers from a set of n O(log n)-bit identifiers, assuming that there are no false deletions of identities not already in the set. Indeed, we give a lower bound argument that shows that any small-space deterministic solution to the straggler identification problem cannot be guaranteed to handle false deletions. Nevertheless, we show that there is a simple randomized solution using O(d log n log(1/epsilon)) bits that can maintain a multiset and solve the straggler identification problem, tolerating false deletions, where epsilon>0 is a user-defined parameter bounding the probability of an incorrect response. This randomized solution is based on a new type of Bloom filter, which we call the invertible Bloom filter.

Connected through hyperedge HE-1 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "fast scalable construction of minimal perfect hash functions"
    - "bubing massive crawling for the masses"
    - "the multiple orientability thresholds for random hypergraphs"
    - "on the insertion time of random walk cuckoo hashing"
    - "high speed hashing for integers and strings"
    - "cache oblivious peeling of random hypergraphs"
    - "thresholds of spatially coupled systems via lyapunov s method"

Connected through hyperedge HE-2 (induced by source paper "dense peelable random uniform hypergraphs"):
  with members:
    - "fast scalable construction of minimal perfect hash functions"
    - "the multiple orientability thresholds for random hypergraphs"
    - "cache oblivious peeling of random hypergraphs"
    - "biff bloom filter codes fast error correction for large data sets"
    - "invertible bloom lookup tables"
    - "sharp load thresholds for cuckoo hashing"
    - "bloomier filters a second look"

Connected through hyperedge HE-3 (induced by source paper "a fast and compact invertible sketch for network wide heavy flow detection"):
  with members:
    - "optimal elephant flow detection"
    - "heavy hitter detection entirely in the data plane"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "amon an open source architecture for online monitoring statistical analysis and forensics of multi gigabit streams"
    - "invertible bloom lookup tables"

Connected through hyperedge HE-4 (induced by source paper "robust set reconciliation via locality sensitive hashing"):
  with members:
    - "reconciling graphs and sets of sets"
    - "wear minimization for cuckoo hashing how not to throw a lot of eggs into one basket"
    - "parallel peeling algorithms"
    - "simple multi party set reconciliation"
    - "invertible bloom lookup tables"

Connected through hyperedge HE-5 (induced by source paper "reconciling graphs and sets of sets"):
  with members:
    - "models and algorithms for graph watermarking"
    - "graph isomorphism in quasipolynomial time"
    - "multi party set reconciliation using characteristic polynomials"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "an efficient reconciliation algorithm for social networks"
    - "simple multi party set reconciliation"
    - "invertible bloom lookup tables"

Connected through hyperedge HE-6 (induced by source paper "hashing for statistics over k partitions"):
  with members:
    - "from independence to expansion and back again"
    - "beyond locality sensitive hashing"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "on the k independence required by linear probing and minwise independence"
    - "biff bloom filter codes fast error correction for large data sets"
    - "invertible bloom lookup tables"

Connected through hyperedge HE-7 (induced by source paper "multi party set reconciliation using characteristic polynomials"):
  with members:
    - "simple multi party set reconciliation"
    - "invertible bloom lookup tables"
    - "analyzing network coding gossip made easy"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-8 (induced by source paper "hardness of peeling with stashes"):
  with members:
    - "biff bloom filter codes fast error correction for large data sets"
    - "invertible bloom lookup tables"
    - "tight thresholds for cuckoo hashing via xorsat"

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)