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: "survey of nearest neighbor techniques"
  Abstract: The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly classified into structure less and structure based techniques. In this paper, we present the survey of such techniques. Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms developed on the basis of kNN. The structure less method overcome memory limitation and structure based techniques reduce the computational complexity.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive survey on the ambulance routing and location problems"):
  with members:
    - "learning combinatorial optimization algorithms over graphs"
    - "playing atari with deep reinforcement learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "effects of distance measure choice on k nearest neighbor classifier performance a review"):
  with members:
    - "on enhancing the performance of nearest neighbour classifiers using hassanat distance metric"
    - "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"
    - "dimensionality invariant similarity measure"
    - "experiments of distance measurements in a foliage plant retrieval system"
    - "content based video retrieval systems"

Connected through hyperedge HE-3 (induced by source paper "regal representation learning based graph alignment"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "learning structural node embeddings via diffusion wavelets"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "role discovery in networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-4 (induced by source paper "generative interest estimation for document recommendations"):
  with members:
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "estimating probabilities in recommendation systems"

Connected through hyperedge HE-5 (induced by source paper "a new classification approach for robotic surgical tasks recognition"):
  with members:
    - "toward personalized training and skill assessment in robotic minimally invasive surgery"
    - "machine learning approach for skill evaluation in robotic assisted surgery"

Connected through hyperedge HE-6 (induced by source paper "classifying documents within multiple hierarchical datasets using multi task learning"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "a model of inductive bias learning"

Connected through hyperedge HE-7 (induced by source paper "unit commitment using nearest neighbor as a short term proxy"):
  with members:
    - "chance constrained outage scheduling using a machine learning proxy"
    - "distributed scenario based optimization for asset management in a hierarchical decision making environment"
    - "hierarchical decision making in electricity grid management"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-8 (induced by source paper "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"):
  with members:
    - "visualizing large scale and high dimensional data"
    - "hashing for similarity search a survey"

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)