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: "hashing for similarity search a survey"
  Abstract: Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-2 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "is second order information helpful for large scale visual recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-3 (induced by source paper "a hybrid compact neural architecture for visual place recognition"):
  with members:
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "what can neural networks reason about"
    - "dashnet a hybrid artificial and spiking neural network for high speed object tracking"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "deep learning features at scale for visual place recognition"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-4 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "benchmarking crimes an emerging threat in systems security"
    - "frappuccino fault detection through runtime analysis of provenance"

Connected through hyperedge HE-5 (induced by source paper "blocking and filtering techniques for entity resolution a survey"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "pigeonring a principle for faster thresholded similarity search"
    - "scalable and robust set similarity join"
    - "set similarity search beyond minhash"
    - "a dnf blocking scheme learner for heterogeneous datasets"
    - "a comparison of blocking methods for record linkage"
    - "distributed representations of words and phrases and their compositionality"
    - "scaling multiple source entity resolution using statistically efficient transfer learning"

Connected through hyperedge HE-6 (induced by source paper "edit distance embedding using convolutional neural networks"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "practical and optimal lsh for angular distance"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-7 (induced by source paper "learning discriminative hashing codes for cross modal retrieval based on multi view features"):
  with members:
    - "learning shared semantic space with correlation alignment for cross modal event retrieval"
    - "semi supervised hashing for semi paired cross view retrieval"
    - "discriminative supervised hashing for cross modal similarity search"
    - "supervised matrix factorization for cross modality hashing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "large scale weakly supervised pre training for video action recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

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)