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: "simd compression and the intersection of sorted integers"
  Abstract: Sorted lists of integers are commonly used in inverted indexes and database systems. They are often compressed in memory. We can use the SIMD instructions available in common processors to boost the speed of integer compression schemes. Our S4-BP128-D4 scheme uses as little as 0.7 CPU cycles per decoded integer while still providing state-of-the-art compression. #R##N#However, if the subsequent processing of the integers is slow, the effort spent on optimizing decoding speed can be wasted. To show that it does not have to be so, we (1) vectorize and optimize the intersection of posting lists; (2) introduce the SIMD Galloping algorithm. We exploit the fact that one SIMD instruction can compare 4 pairs of integers at once. #R##N#We experiment with two TREC text collections, GOV2 and ClueWeb09 (Category B), using logs from the TREC million-query track. We show that using only the SIMD instructions ubiquitous in all modern CPUs, our techniques for conjunctive queries can double the speed of a state-of-the-art approach.

Connected through hyperedge HE-1 (induced by source paper "permute to train a new dimension to training deep neural networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "weight agnostic neural networks"
    - "what s hidden in a randomly weighted neural network"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "compressed data structures for binary relations in practice"):
  with members:
    - "compressed representation of dynamic binary relations with applications"
    - "compact binary relation representations with rich functionality"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-3 (induced by source paper "learning multi stage sparsification for maximum clique enumeration"):
  with members:
    - "fine grained search space classification for hard enumeration variants of subset problems"
    - "neural combinatorial optimization with reinforcement learning"
    - "decoding billions of integers per second through vectorization"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-4 (induced by source paper "on the reproducibility of experiments of indexing repetitive document collections"):
  with members:
    - "reproducible experiments on dynamic resource allocation in cloud data centers"
    - "universal indexes for highly repetitive document collections"
    - "a collaborative approach to computational reproducibility"

Connected through hyperedge HE-5 (induced by source paper "faster population counts using avx2 instructions"):
  with members:
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "better bitmap performance with roaring bitmaps"

Connected through hyperedge HE-6 (induced by source paper "stream vbyte faster byte oriented integer compression"):
  with members:
    - "vectorized vbyte decoding"

Connected through hyperedge HE-7 (induced by source paper "roaring bitmaps implementation of an optimized software library"):
  with members:
    - "faster population counts using avx2 instructions"
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "array layouts for comparison based searching"
    - "better bitmap performance with roaring bitmaps"
    - "compressed bitmap indexes beyond unions and intersections"
    - "reordering rows for better compression beyond the lexicographic order"
    - "sorting improves word aligned bitmap indexes"

Connected through hyperedge HE-8 (induced by source paper "upscaledb efficient integer key compression in a key value store using simd instructions"):
  with members:
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "a general simd based approach to accelerating compression algorithms"
    - "vectorized vbyte decoding"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"

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.IR(Information Retrieval)