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: "efficient hashing with lookups in two memory accesses"
  Abstract: The study of hashing is closely related to the analysis of balls and bins. It is well-known that instead of using a single hash function if we randomly hash a ball into two bins and place it in the smaller of the two, then this dramatically lowers the maximum load on bins. This leads to the concept of two-way hashing where the largest bucket contains $O(\log\log n)$ balls with high probability. The hash look up will now search in both the buckets an item hashes to. Since an item may be placed in one of two buckets, we could potentially move an item after it has been initially placed to reduce maximum load. with a maximum load of We show that by performing moves during inserts, a maximum load of 2 can be maintained on-line, with high probability, while supporting hash update operations. In fact, with $n$ buckets, even if the space for two items are pre-allocated per bucket, as may be desirable in hardware implementations, more than $n$ items can be stored giving a high memory utilization. We also analyze the trade-off between the number of moves performed during inserts and the maximum load on a bucket. By performing at most $h$ moves, we can maintain a maximum load of $O(\frac{\log \log n}{h \log(\log\log n/h)})$. So, even by performing one move, we achieve a better bound than by performing no moves at all.

Connected through hyperedge HE-1 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses"):
  with members:
    - "bloom filters adaptivity and the dictionary problem"
    - "adaptive cuckoo filters"
    - "fast scalable construction of minimal perfect hash functions"
    - "dynamic integer sets with optimal rank select and predecessor search"
    - "how to approximate a set without knowing its size in advance"
    - "don t thrash how to cache your hash on flash"
    - "the power of simple tabulation hashing"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-2 (induced by source paper "cuckoo hash tables high performance hash tables for networking applications"):
  with members:
    - "emoma exact match in one memory access"

Connected through hyperedge HE-3 (induced by source paper "scalability and total recall with fast coveringlsh"):
  with members:
    - "locality sensitive hashing without false negatives"
    - "fast exact search in hamming space with multi index hashing"
    - "bayesian locality sensitive hashing for fast similarity search"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "b bit minwise hashing"
    - "lower bounds on locality sensitive hashing"

Connected through hyperedge HE-4 (induced by source paper "a cuckoo hashing variant with improved memory utilization and insertion time"):
  with members:
    - "the multiple orientability thresholds for random hypergraphs"
    - "cuckoo hashing with pages"
    - "tight thresholds for cuckoo hashing via xorsat"

Connected through hyperedge HE-5 (induced by source paper "external memory multimaps"):
  with members:
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"

Connected through hyperedge HE-6 (induced by source paper "backyard cuckoo hashing constant worst case operations with a succinct representation"):
  with members:
    - "sharp load thresholds for cuckoo hashing"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"
    - "succinct data structures for retrieval and approximate membership"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-7 (induced by source paper "succinct data structures for retrieval and approximate membership"):
  with members:
    - "an optimal bloom filter replacement based on matrix solving"

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)