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: "inner product similarity search using compositional codes"
  Abstract: This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group $M$-selection algorithm that selects $M$ elements from $M$ source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets ($1M$ and $1B$ SIFT features, $1M$ linear models and Netflix) demonstrate the superiority of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "deep triplet quantization"):
  with members:
    - "subic a supervised structured binary code for image search"
    - "hashnet deep learning to hash by continuation"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a survey on learning to hash"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "scalable discrete supervised hash learning with asymmetric matrix factorization"
    - "approximate search with quantized sparse representations"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-3 (induced by source paper "group k means"):
  with members:
    - "optimized cartesian k means"

Connected through hyperedge HE-4 (induced by source paper "accelerated distance computation with encoding tree for high dimensional data"):
  with members:
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-5 (induced by source paper "hashing for similarity search a survey"):
  with members:
    - "optimized cartesian k means"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "beyond locality sensitive hashing"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "coding for random projections"
    - "a general two step approach to learning based hashing"
    - "on the difficulty of nearest neighbor search"

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.CV(Computer Vision and Pattern Recognition)