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: "complementme weakly supervised component suggestions for 3d modeling"
  Abstract: Assembly-based tools provide a powerful modeling paradigm for non-expert shape designers. However, choosing a component from a large shape repository and aligning it to a partial assembly can become a daunting task. In this paper we describe novel neural network architectures for suggesting complementary components and their placement for an incomplete 3D part assembly. Unlike most existing techniques, our networks are trained on unlabeled data obtained from public online repositories, and do not rely on consistent part segmentations or labels. Absence of labels poses a challenge in indexing the database of parts for the retrieval. We address it by jointly training embedding and retrieval networks, where the first indexes parts by mapping them to a low-dimensional feature space, and the second maps partial assemblies to appropriate complements. The combinatorial nature of part arrangements poses another challenge, since the retrieval network is not a function: several complements can be appropriate for the same input. Thus, instead of predicting a single output, we train our network to predict a probability distribution over the space of part embeddings. This allows our method to deal with ambiguities and naturally enables a UI that seamlessly integrates user preferences into the design process. We demonstrate that our method can be used to design complex shapes with minimal or no user input. To evaluate our approach we develop a novel benchmark for component suggestion systems demonstrating significant improvement over state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-2 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "learning to infer and execute 3d shape programs"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "grains generative recursive autoencoders for indoor scenes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-3 (induced by source paper "learning part generation and assembly for structure aware shape synthesis"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "structure aware shape synthesis"
    - "learning fuzzy set representations of partial shapes on dual embedding spaces"
    - "learning implicit fields for generative shape modeling"
    - "learning to generate the unseen via part synthesis and composition"

Connected through hyperedge HE-4 (induced by source paper "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"):
  with members:
    - "learning to group and label fine grained shape components"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "point convolutional neural networks by extension operators"
    - "grains generative recursive autoencoders for indoor scenes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "scores shape composition with recursive substructure priors"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-5 (induced by source paper "cosegnet deep co segmentation of 3d shapes with group consistency loss"):
  with members:
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "deep object co segmentation"
    - "faces as lighting probes via unsupervised deep highlight extraction"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d shape segmentation via shape fully convolutional networks"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "tags2parts discovering semantic regions from shape tags"

Connected through hyperedge HE-6 (induced by source paper "learning to group and label fine grained shape components"):
  with members:
    - "learning hierarchical shape segmentation and labeling from online repositories"
    - "deformation driven shape correspondence via shape recognition"
    - "3d shape segmentation with projective convolutional networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "shapenet an information rich 3d model repository"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "generating 3d adversarial point clouds"):
  with members:
    - "spatially transformed adversarial examples"
    - "a deeper look at 3d shape classifiers"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "dynamic graph cnn for learning on point clouds"
    - "generating adversarial examples with adversarial networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "p2p net bidirectional point displacement net for shape transform"):
  with members:
    - "pcpnet learning local shape properties from raw point clouds"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unsupervised image to image translation networks"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "shape generation using spatially partitioned point clouds"
    - "dualgan unsupervised dual learning for image to image translation"

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.GR(Graphics)